A strong end to its current fiscal year kept Wall Street happy with Microsoft, despite a 70% year-on-year increase in CapEx to fund AI infrastructure expansion.

Total Q4 revenue of $89.5 billion dollars was up 17% year-on-year, while net income rose 24% at $27.2 billion. Revenue from Microsoft Cloud grew 27% to $56.5 billion dollars, within which Azure revenue was up 39%.

It was a strong end to the fiscal year, said CEO Satya Nadella:

All up, our annual revenue surpassed $331 billion, up 18%. Microsoft Cloud surpassed $214 billion, up 27% and Azure surpassed $100 billion, up 41%. Going forward, we have two goals; first, ensuring AI empowers every person, amplifying their agency and ambition; and second, empowering every organization to build their own continuous learning loop and ensuring that they don't outsource their core IP.

In terms of the CapEx to fund AI infrastructure growth, spend for the quarter hit $41 billion, roughly two-thirds of which went on ‘short-lived assets’, primarily CPUs and GPUs. Nadella confirmed that the firm’s expansion plans remain on track, despite the negativity from Wall Street:

We added 31 new data centers across five continents this quarter, bringing the total to 88 this year as we expand our footprint in response to accelerating demand. We are also bringing capacity online faster than ever. Over the last fiscal year, we have reduced dock-to-live times for new GPUs in our largest regions by nearly 50%. All up, we added another gigawatt of capacity this quarter and remain on track to roughly double our overall capacity in just two years. We're also getting more from the infrastructure we already have by optimizing across silicon, systems, and software. For example, we increased the throughput for Copilot workloads 4x since the start of the year.

Copilot use remains on the rise, according to Nadella:

The number of conversations per user nearly doubled year-over-year. Average weekly engagement is on par with Outlook and Teams. And the time from deployment to what we think of as high usage, meaning monthly active usage about 80% across a customers' user base, has fallen from months to just days over the past year.

The number of customers with more than 50,000 seats increased over 7x year-over-year, and the number of enterprise customers deploying Copilot to the majority of their information workers grew nearly 75% quarter-over-quarter, a signal of how central Copilot has become to their operations. NHS England, for example, is rolling out Copilot to 505,000 clinicians and staff, the largest health care deployment of its kind after a trial showed it saved employees an average of 43 minutes per day. KPMG is expanding its deployment across its global workforce of more than 276,000 professional, and HSBC committed to 200,000 seats to accelerate its workforce transformation.

The current attention being paid to the need to have sovereign tech capabilities is also a focus, Nadella confirmed:

AI sovereignty is increasingly top of mind for our customers, and we are expanding our offerings to meet that need. Just last week, we announced a partnership with Mistral to bring its models to Microsoft Sovereign Cloud, enabling customers to run them across public, customer-controlled, and fully dis-connected environments.

Model choice is important to customers, he added:

 Every customer wants the right model for each task based on quality, latency, cost, and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the latest from OpenAI, Anthropic, Mistral, xAI, as well as our own MAI family. Since the start of the year, we have seen 5x increase in the number of customers building with models from multiple providers. Levi Strauss & Co., for example, is using models from OpenAI and Anthropic on Foundry as it brings more than 1,000 domain-specific agents into a unified enterprise AI platform.

We are also accelerating our own model development. We announced more than a dozen new models across image, voice, transcription, coding, security, including our first reasoning model MAI-Thinking-1, all with cost-efficient inference at the core for the enterprise use cases. We are co-designing these models with our silicon, and we are seeing 40% better performance per watt when running MAI models on Maia 200. But more importantly, we are building a new model system where the harness, context, memory, and action space are separate from any one model family, thereby moving the frontier on the cost-to-outcome curve.

There are important lessons being learned here, said Nadella:

We are very, very clear about the architectural design of the platform, which is you've got to keep your harness separate from the model. When the harness will ensure that your memory, your context, all of that is external. That means any given model at any given time is swappable. You should and you can use frontier models. There's no reason not to. But you also can use multiples of them.

So if you look at some of the stats I gave. It's a great example of how to use the frontier models for what they deliver, how to use low-cost models for what they deliver, and in fact, train your own model when you don't want to use any external model itself because after all, you have all the output, you have all the traces, you have all the context. That's really the enterprise design architecture that we are going to evangelize.

A good end to the year for Microsoft, with Wall Street short-termists diverting their panic attacks in the direction of Meta which reported its numbers within hours of Nadella’s firm.