MICROSOFT--FH25--Eyes on another Monopoly?

MICROSOFT FH25 result

Summary

There is little requirement to describe MSFT in detail, it is one of the best-known companies and one of, IMO, one of the best-unregulated monopolies in public markets. The big questions are based around is the strategy correct and is the valuation reasonable.

MSFT has moved reasonably aggressively to be the main platform for AI. That goal is taking considerable capital and it is deploying into a moving feast. As that story pans out we are likely to see optimism and pessimism, to various degrees. The goal is attainable both technically and financially by MSFT, and the goal should also be lucrative for the group.

MSFT is involved along the stack, with the LLM market largely through Open AI, it is also in the agent deployment with the Copilot family and is also interested in becoming the AI platform of choice. Headway is being made across all fronts. AI revenue it was disclosed is at a $13B run rate, about 5% of total revenues but growing very quickly.

MSFT needs AI ubiquity by making agents cost-effective and easy to implement and use. Anything that promotes this story will assist MSFT in reaching its goals. Competition is limited across the whole stack, GOOG and AMZN could potentially have the reach, finances and technology, but MSFT has taken the lead in GTM and it is unlikely to give that lead-up.

Management at MSFT is exceptional and fully understands capital returns and deployment.

Market concerns are,

1. The capex spend is huge, perhaps surpassing anything that corporate America has seen. That is putting pressure on FCF, defined as CFO less capex. Two things to note in the last six months, CFO was $56B and PPE capex $31B, so it can be financed. Secondly, management has repeatably said that the spending is fungible, the meaning is that it can be used in various ways and can be adjusted to align with underlying customer demand. MSFT has an option to adjust this spend it is not an SIB requirement.

2. The more important concern is whether adequate returns can be generated. There are many moving parts in this puzzle and it will be some time before there is a large degree of proof, one way or another. Remembering that MST is playing across the stack, so overall AI market growth is key, not a specific product or segment.

Valuation

Due to its sheer size, it takes a lot to move the dial for MSFT. AI dominance is one of the few avenues for growth. Growth should continue at 10-15% pa with a low level of uncertainty. The clear test will be if MSFT can maintain ROE as it deploys the massive capital involved.

My base case is 14% EPS growth for the next 5 years and an exit multiple of 26X, noting that 24X is about as low as it has gone in the last five years. To generate a 10% return would require a SP of $375. If the AI story holds, I think the outcomes are lower risk and may be conservative, so $375 is a solid entry/add. You could easily support a $400 SP.

In some ways, MSFT is a safe option with steady but attractive growth, it is expected to be there at the end in a strong position.

CALL SUMMARY

Satya Nadella CEO

This quarter we saw continued strength in Microsoft Cloud, which surpassed $40 billion in revenue for the first time, up 21% year-over-year. Enterprises are beginning to move from proof-of-concepts to enterprise-wide deployments to unlock the full ROI of AI. And our AI business has now surpassed an annual revenue run rate of $13 billion up 175% year-over-year.

On inference, we have typically seen more than 2x price performance gain for every hardware generation and more than 10x for every model generation due to software optimizations. And as AI becomes more efficient and accessible, we will see exponentially more demand.

Therefore, much as we have done with the Commercial Cloud, we are focused on continuously scaling our fleet globally and maintaining the right balance across training and inference as well as geo-distribution. From now on it's a more continuous cycle governed by both revenue growth and capability growth, thanks to the compounding effects of software driven AI scaling laws and Moore's Law.

Azure is the infrastructure layer for AI. We continue to expand our data centre capacity in line with both near-term and long-term demand signals

Our data centres, networks, racks and silicon are all coming together as a complete system to drive new efficiencies to power both the cloud workloads of today and the next generation AI workloads.

At the data layer, we are seeing Microsoft Fabric breakout. Fabric is now the fastest growing analytics product in our history. Power BI is also deeply integrated with Fabric with over 30 million monthly active users, up 40% since last year.

The number of Azure OpenAI apps running on Azure databases and Azure app services more than doubled year-over-year driving significant growth and adoption across SQL Hyperscale and Cosmos DB.

Azure AI Foundry features best-in-class tooling, run times to build agents, multi-agent apps, AI ops, API access to thousands of models. Two months in, we already have more than 200,000 monthly active users and we are well positioned with our support of both OpenAI's leading models and the best selection of Open Source models and SLMs.

All up GitHub now is home to 150 million developers up 50% over the past two years.

Copilot

Microsoft 365 Copilot is the UI for AI. It helps supercharge employee productivity and provides access to a swarm of intelligent agents to streamline employee workflow. We are seeing accelerated customer adoption across all deal sizes as we win new Microsoft 365 Copilot customers and see the majority of existing enterprise customers come back to purchase more seats. When you look at customers who purchased Copilot during the first quarter of availability, they have expanded their seats collectively by more than 10x over the past 18 months. Usage intensity increased more than 60% quarter-over-quarter and we are expanding our TAM with Copilot Chat which was announced earlier this month. Copilot Chat along with Copilot Studio is now available to every employee to start using agents right in the flow of work. With Copilot Studio, we are making it as simple to build an agent as it is to create an Excel spreadsheet. More than 160,000 organizations have already used Copilot Studio and they collectively created more than 400,000 custom agents in the last three months alone up over 2x quarter-over-quarter. We've also introduced our own first-party agents to facilitate meetings, manage projects, resolve common HR and IT queries and access SharePoint data. We also continue to see partners like Adobe, SAP, ServiceNow and Workday build their third-party agents and integrate with Copilot. What is driving Copilot as the UI for AI as well as our momentum with agents is our rich data cloud which is the world's largest source of organizational knowledge. Billions of emails, documents and chats, hundreds of millions of team meetings and millions of SharePoint sites are added each day. This is the Enterprise Knowledge Cloud. It is growing fast up over 25% year-over-year. More broadly what we are seeing is Copilot plus agents disrupting business applications and we are leaning into this. 15% of premium-priced laptops in the US this holiday were Copilot+ PCs and we expect the majority of the PCs sold in the next several years to be Copilot+ PCs. We're also innovating with agents to help recruiters and small businesses find qualified candidates faster and our hiring business again took share.

Amy Hood CFO

We delivered another quarter of double-digit top and bottom-line growth. Results were driven by strong demand for our cloud and AI offerings, while we also improved our operating leverage with higher-than-expected operating income growth.

Commercial bookings increased 67% and 75% in constant currency and were significantly ahead of expectations driven by Azure commitments from OpenAI.

Commercial remaining performance obligation increased to $298 billion, up 34% and 36% in constant currency.

Microsoft Cloud revenue was $40.9 billion (ed. about 29% of total) and grew 21%. Microsoft Cloud gross margin percentage was 70%, in line with expectations and decreased two points year-over-year driven by scaling our AI infrastructure.

Revenue from Productivity and Business Processes was $29.4 billion and grew 14% Results were ahead of expectations driven by Microsoft 365 Commercial.

Microsoft 365 Commercial Cloud revenue increased 16%, slightly ahead of expectations due to better-than-expected performance in E5 and Microsoft 365 Copilot. With M365 Copilot, we continue to see growth in adoption, expansion, and usage. Segment gross margin dollars increased 13%, gross margin percentage decreased slightly year-over-year driven by scaling our AI infrastructure.

Intelligent Cloud segment. Revenue was $25.5 billion and grew 19% in Azure non-AI services, on-prem server, and enterprise and partner services were slightly lower than expected, partially offset by better-than-expected results in Azure AI services. Azure and other cloud services revenue grew 31%. Azure growth included 13 points from AI services, which grew 157% year-over-year and was ahead of expectations even as demand continued to be higher than our available capacity. Growth in our non-AI services was slightly lower than expected due to go-to-market execution challenges, particularly with our customers that we primarily reach through our scale motions, as we balance driving near-term non-AI consumption with AI growth. Segment gross margin dollars increased 12%, gross margin percentage decreased four points year-over-year driven by scaling our AI infrastructure.

More Personal Computing. Revenue was $14.7 billion, relatively unchanged year-over-year with better than expected results driven primarily by Windows OEM pre-builds, usage from a third-party partnership in Search, as well as Call of Duty launch performance in Gaming. Segment gross margin dollars increased 13%. Gross margin percentage increased six points year-over-year driven by a sales mix shift to higher margin businesses as well as strong execution on margin improvement in Gaming and Search. Operating income increased 32% driven by continued prioritization of higher margin opportunities.

Capital expenditures including finance leases were $22.6 billion, in line with expectations, and cash paid for PP&E was $15.8 billion. More than half of our cloud and AI related spend was on long-lived assets that will support monetization over the next 15 years and beyond. The remaining cloud and AI spend was primarily for servers, both CPUs and GPUs, to serve customers based on demand signals including our customer contracted backlog. Free cash flow was $6.5 billion, down 29% year-over-year, reflecting the capital expenditures noted earlier.

When compared to our October guidance assumptions on Q3 FX impact, this is a decrease to total revenue of roughly $1 billion.

As a reminder, larger long-term Azure contracts, which are more unpredictable in their timing, can drive increased quarterly volatility in our bookings growth rate. Microsoft Cloud gross margin percentage should be roughly 69%, down year-over-year driven by the impact of scaling our AI infrastructure.

Q325 Guidance

In Productivity and Business Processes we expect revenue to grow between 11% and 12% in constant currency or $29.4 billion to $29.7 billion. Microsoft 365 Commercial Cloud revenue growth should be between 14% and 15% in constant currency, relatively stable compared to our better-than-expected Q2 results.

For Intelligent Cloud we expect revenue to grow between 19% and 20% in constant currency or $25.9 billion to $26.2 billion. Revenue will continue to be driven by Azure which, as a reminder, can have quarterly variability primarily from in-period revenue recognition depending on the mix of contracts. In Azure, we expect Q3 revenue growth to be between 31% and 32% in constant currency driven by strong demand for our portfolio of services. As we shared in October, the contribution from our AI services will grow from increased AI capacity coming online. In non-AI services healthy growth continues, although we expect ongoing impact through H2 as we work to address the execution challenges noted earlier. And while we expect to be AI capacity constrained in Q3, by the end of FY25 we should be roughly in line with near-term demand given our significant capital investments.

In More Personal Computing, we expect revenue to be $12.4 billion to $12.8 billion with continued prioritization of higher margin opportunities. Windows OEM and Devices revenue should decline in the low to mid-single-digits. We expect revenue from Windows OEM to be relatively flat year-over-year as our outlook assumes inventory levels will normalize.

Next, capital expenditures. We expect quarterly spend in Q3 and Q4 to remain at similar levels as our Q2 spend. In FY26, we expect to continue investing against strong demand signals including customer contracted backlog we need to deliver against across the entirety of our Microsoft Cloud. However, the growth rate will be lower than FY25 and the mix of spend will begin to shift back to short-lived assets which are more correlated to revenue growth.

For the full fiscal year, we continue to expect double-digit revenue and operating income growth as we focus on delivering efficiencies across both COGS and operating expense. And given the operating leverage that we've delivered throughout the year, inclusive of efficiency gains as we scale our AI infrastructure and utilize our own AI solutions, we now expect FY25 operating margins to be up slightly year-over-year.

Question-and-Answer Session

Let me spend a little time on that about what we saw in Q2 and give you some additional background on the near-term execution issues that we're talking about. First, let me be very specific. They are in the non-AI ACR component. Our Azure AI results were better than we thought due to very good work by the operating teams pulling in some delivery dates even by weeks. When your capacity constrained weeks matter, and it was good execution by the team, and you see that in the revenue results. On the non-AI side, really, the challenges were in what we call the scale motion. So think about primarily these are customers we reach through partners and through more indirect methods of selling. And really, the art form there is as these customers, which we reach in this way, are trying to balance how do you do an AI workload with continuing some of the work they've done on migrations and other fundamentals, we then took our sales motions in the summer and really change to try to balance those two. As you do that, you learn with your customers and with your partners on sort of getting that balance right between where to put our investments, where to put the marketing dollars and importantly, where to put people in terms of coverage and being able to help customers make those transitions. And I think we are going to make some adjustments to make sure we are in balance because when you make those changes in the summer, by the time it works its way through the system, you can see the impacts on whether you have that balance right. And I expect, while, we will see some impact through H2 just because when you work through the scale motion, it can take some time for that to adjust. Ed. MSFT simultaneously trying to coordinate client cloud migration and AI implementation through third parties causing issues.

And when I talk about being at a capacity constraint (in AI DCs), it takes two things. You have to have space, which I generally call long-lived assets, that's the infrastructure and the land and then you have to have kits. We're continuing and you've seen that's why our spending has pivoted this way to be in the long-lived investment. We have been short, of power and space. And so as you see those investments land that we've made over the past three years, we get closer to that balance by the end of this year. And so the confidence on the AI side continues to be there in terms of being able to sell, utilize and be, I think, encouraged by the signals. What we're seeing is waiting to see just how the non-AI ACR works through the scale motions in H2. But in general, the only thing that's changed is really that scale motion from my seat.

Number one is the Azure component was better. And the second piece, you're right, Microsoft Copilot was better. And what was important about that it was -- we saw strength both in seats, both new seats and expansion seats, and usage, which doesn't directly impact revenue, but of course, indirectly does as people get more and more value on it. And also the price per seat was actually quite good. We still have a good signal for value.

DeepSeek has had some real innovations. And that is some of the things that even OpenAI found in o1. And so we are going to, obviously, now that all gets commoditized, and it's going to get broadly used. And the big beneficiaries of any software cycle like that are the customers. Because at the end of the day, if you think about it, what was the big lesson learned from client server to cloud, more people bought servers except it was called cloud. And so when token prices fall, in fact, computing prices fall, that means people can consume more, and there'll be more apps written. And it's interesting to see that when I referenced these models that are pretty powerful, it's unimaginable to think that here we are in sort of beginning of '25 where on the PC, you can run a model that required pretty massive cloud infrastructure. So that type of optimizations means AI will be much more ubiquitous. And so therefore, for a hyperscaler like us, a PC platform provider like us, this is all good news as far as I'm concerned.

we remain very happy with the partnership with OpenAI. And as you saw, they have committed in a big way to Azure and even in the bookings what we recognize is just the first tranche of it. And so you'll see given the Right of First Refusal we have more benefits of that even into the future. And obviously, their success is our success. But to your overall point, the thing that I would say is we are building a pretty fungible fleet. We're making sure that there's the right balance between training and inference. It's geo-distributed. We are working super hard on all the software optimizations. One of the key things to note in AI is you just don't launch the frontier model, but if it's too expensive to serve, it's no good. It won't generate any demand. So you've got to have that optimization, so that inferencing costs are coming down and they can be consumed broadly. And also, remember, you don't want to buy too much of anything at one time because the Moore's Law every year is going to give you 2x, and your optimization is going to give you 10x. You want to continuously upgrade the fleet, modernize the fleet age, and at the end of the day, have the right ratio of monetization and demand-driven monetization to what you think of as the training expense. So I feel very good about the investment we are making and it's fungible and it just allows us to scale more long-term business.

And then there is running the commercial hub, which at every modern AI app that's going to be built, it will be required. It will be required to be distributed, and it will be required to be global. And all of those things are really important because it then means you're the most efficient. The front end has been this sort of infrastructure build that lets us really catch up not just on the AI infrastructure we needed, but think about that as the building itself, data centres, but also some of the catch-up we need to do on the commercial cloud side. And then you'll see the pivot to more CPU and GPU. And that pivot will more directly correlate to revenue and it will be contracted either with the partnership with OpenAI or with others. And so I do think the way I want everyone to internalize it is that the CapEx growth is going through that cycle pivot, which is far more correlated to customer contract delivery, no matter who the end customer is.

So one of the things that we are investing heavily on is foundry because from an app developer perspective, you kind of want to keep pace with the flurry of models that are coming in and you want to have an evergreen way for your application to benefit from all that innovation. (ed. Platform matching App to Models)

Copilot usage--the clients' initial deployment of seats was for places where there's more belief in immediate productivity, a sales team, in finance or in the supply chain, where there is a lot of like, for example, SharePoint rounded data that you want to be able to use in conjunction with web data and have it produce results that are beneficial. But then what's happening, very much like what we have seen in the previous generation productivity things is that people collaborate across functions. And that pattern then requires you to make it more of a standard issue across the enterprise. It starts maybe at a department level. Quickly, the collaboration network effects will effectively demand that you spread it across the company. And so we've made it easier. And so that gives the enterprise customers even more flexibility to have something that's more ubiquitous.

Commitments to order book--We talked a little bit about what are the main drivers, which was one of the Azure commitments that OpenAI has made and you'll continue to see OpenAI making commitments. we did have very good renewals of our existing contracts, plus add-ons to those contracts upsell like, for example, Copilot or GitHub Copilot or other processes. We also had a good E5 quarter. And then the final component is the large Azure commitments. And those really did look as we expected, which is good. Those Azure commitments take two forms. One, it's existing customers who've worked through their commitments and are making larger commitments, which is a good commitment signed for the platform. And then you have new customers making commitments, and we also saw that this quarter.

IC includes AI and cloud growth. It is expected to largely drive results, MPC (gaming, search, Windows, advertising) is more consumer-facing and is more cyclical. P&BP (Microsoft 365 suite, Teams) a steady grower.