1. Mistaking Azure Credits for Cash
The most fundamental error is treating the billions in Azure cloud credits Microsoft gives OpenAI as direct cash revenue. It's not. Microsoft's massive investment in OpenAI includes a significant portion of these credits, which OpenAI then uses to pay
for the immense computing power required to train and run its models. While this usage is recognized as Azure revenue on Microsoft's books, it's more like a circular financial loop than a traditional customer sale. An analyst at wheresyoured.at estimated that for fiscal year 2026, roughly 70% of Microsoft's reported AI revenue came from OpenAI spending money that substantially originated with Microsoft itself. This isn't a secret or fraudulent, but it inflates the picture of organic, external demand for Azure's AI services. The real cash from OpenAI comes from other parts of the deal.
2. Oversimplifying the Revenue and Profit Share
The phrase 'Microsoft gets a cut of OpenAI's revenue' papers over crucial details. The deal has multiple, complex layers. For a period, Microsoft received a 20% share of much of OpenAI's revenue. However, the deal has been amended. Recent agreements confirm OpenAI's revenue share payments to Microsoft will continue through 2030 but are now subject to a total cap. Furthermore, another part of the deal involves a share of OpenAI's profits, not revenue. Given that OpenAI has been posting substantial losses to fund its growth, a share of a negative or near-zero number is worth very little in the present. Conflating a capped revenue share with an uncapped profit share leads to wildly inaccurate models of near-term cash flow to Microsoft.
3. Ignoring the Cost of Goods Sold
Booking billions in AI-related revenue looks great on a spreadsheet, but that revenue comes at a staggering cost. Running the massive data centers full of high-end GPUs needed for AI models is an incredibly expensive, energy-intensive operation. This shows up as Microsoft's capital expenditures (CapEx) and cost of goods sold (COGS). Some analysts question whether the standard 5-6 year depreciation schedule for hardware is appropriate for GPUs under constant, heavy training loads, suggesting their functional life might be closer to 2-3 years. If the hardware wears out faster than it's depreciated, the true costs are being understated. One analyst noted that an accounting change extending the useful life of Microsoft's servers and data centers from 15 to 25 years had the effect of lowering the reported CapEx forecast by $15 billion in calendar year 2026 without any change in actual spending plans. Ignoring these massive underlying costs gives a skewed view of the profitability of Microsoft's AI endeavors.
4. Misunderstanding Copilot's Business Model
Analysts often treat Microsoft's Copilot revenue as a simple, high-margin software sale. The reality is more complex. First, there's the cost. For its own Copilot products built on OpenAI models, Microsoft has a royalty-free license through 2032. This is a massive, hard-to-quantify cost saving. Second, the revenue model is shifting. While the initial push was a flat $30 per-user monthly fee, Microsoft is moving toward a more complex, usage-based billing model with 'Copilot Credits'. More advanced features, like the agentic 'Cowork' tool, are metered, meaning costs can become unpredictable for customers and revenue is less of a straight line for Microsoft. Simply multiplying potential users by $30 is a model that is quickly becoming obsolete.
5. Assuming Azure is the Only Cloud in Town
A common assumption was that Microsoft's investment gave it permanent and total exclusivity, forcing OpenAI to run everything on Azure forever. This was a key part of the initial investment thesis. However, the partnership has evolved. As of an amended agreement in April 2026, the deal is now non-exclusive, and OpenAI can officially serve its products across any cloud provider, including rivals like Amazon Web Services. While Azure remains the 'primary' cloud partner, this change introduces competitive dynamics and reduces the certainty of future Azure consumption. For instance, OpenAI has already said its Oracle-operated 'Abilene' site was training and serving frontier AI systems. Any model that assumes 100% of OpenAI's future compute spend will land on Azure is now demonstrably false.
6. Attributing All AI Growth to OpenAI
While OpenAI is a huge driver, it's a mistake to attribute all of Microsoft's AI success to the partnership. In a Q2 2026 report, Microsoft's remaining performance obligations (RPO) surged to $625 billion, with about 45% attributed to OpenAI, but the company also noted a boost from rival AI lab Anthropic. Microsoft is also developing its own powerful in-house models, like MAI, and has started a $2.5 billion business unit specifically to help customers use multiple models from various providers, not just OpenAI. The CFO of Microsoft's commercial business even admitted, 'we made a mistake by binding it to OpenAI models only.' Focusing solely on the OpenAI relationship misses the bigger picture of Microsoft positioning itself as a broad-based AI platform, hedging its bets beyond a single, albeit critical, partner.
