Microsoft's latest earnings call was a symphony of AI-optimism, punctuated by Satya Nadella's infectious enthusiasm and Amy Hood's reassuringly pragmatic financial projections. The headline? Another quarter of double-digit growth, driven by the relentless force that is Microsoft Cloud. But buried within the torrent of AI pronouncements, a subtle signal emerged, one that seems to have flown under the radar of even the most seasoned analysts.

This signal points towards an impending inflection point, one where Azure's already impressive AI dominance is poised to go stratospheric. We're not talking about a gradual upswing, but a potential rocket-ship trajectory fueled by a unique confluence of factors. Let's unpack this hidden signal.

The first clue lies in the consistent emphasis on "inferencing," the process of using trained AI models to generate outputs. Both Nadella and Hood repeatedly stressed that the impressive 6-point growth contribution from AI services in Azure was primarily driven by inferencing workloads. This is crucial because it reveals the core of Azure's AI advantage: it's not just about training models, but about making them work for customers, at scale.

Think about it: businesses don't want to simply play around with AI models. They want tangible results: increased productivity, better insights, optimized operations. And this is where Azure shines. It provides the infrastructure, the tools, and the ecosystem to deploy AI models and translate their potential into real-world impact.

The second clue comes from Nadella's revealing admission: almost none of the large model training revenue is reflected in the Azure numbers yet. This means that the current 6-point AI growth contribution is just the tip of the iceberg. As more businesses embrace large-scale AI training on Azure, this contribution could skyrocket. Imagine the impact when large model training becomes as ubiquitous as inferencing!

But there's a third, even more compelling clue, and this is where the hidden signal truly reveals itself. Remember Amy Hood mentioning that Azure is facing capacity constraints on the consumption side, meaning the demand for inferencing is outstripping supply? This, coupled with the emphasis on prioritizing capacity for per-user businesses like Microsoft 365 Copilot, paints a fascinating picture.

Here's the hypothesis: Microsoft is deliberately holding back on unleashing the full potential of Azure AI on the consumption side. They are funneling their limited capacity towards the per-user businesses, betting on the rapid adoption of Copilot across Microsoft 365 and other applications. This is a strategic masterstroke.

By prioritizing Copilot, Microsoft is achieving two critical goals. First, they are driving faster adoption of Copilot by ensuring a smooth user experience, unhindered by capacity limitations. Second, they are priming the pump for a future explosion in Azure AI consumption.

Think about millions of users across businesses becoming accustomed to Copilot's capabilities within Microsoft 365. As their reliance on Copilot grows, so too will their demand for more sophisticated AI solutions. And where will they naturally turn to satisfy that demand? Azure, of course!

This is the hidden signal: a deliberate strategy to create a massive pent-up demand for Azure AI. As Microsoft scales up its infrastructure and addresses the capacity constraints, this pent-up demand could erupt, driving a surge in Azure AI consumption far beyond the current 6 points.

Projected Growth Contribution from AI within Azure

The following chart is a hypothetical projection of AI's contribution to Azure's revenue growth, based on the analysis of Microsoft's earnings calls. This projection assumes a 50% attach rate for Microsoft 365 Copilot by the end of FY2025, with an average ARPU of $5 per user, and a contribution from large model training that matches or exceeds the inferencing contribution.

Here's the potential impact in numbers: if Microsoft 365 Copilot sees a 50% attach rate by the end of FY2025, with an average ARPU of $5 per user, this could translate to an additional $2 billion in revenue per quarter. And this is just for Copilot.

Add in the potential contribution from large model training, which could easily match or exceed the inferencing contribution, and you're looking at a potential 12-point or higher growth contribution from AI within Azure by the end of FY2025. This is not a wild prediction, but a logical extrapolation based on the current trends and the hidden signal in Microsoft's earnings call.

The question is, are investors ready for this AI-powered explosion in Azure's dominance?

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