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Greetings, and welcome to the Microsoft Fiscal Year 2026 Third Quarter Earnings Conference Call. [Operator Instructions] As a reminder, this conference is being recorded. It is now my pleasure to introduce Jonathan Neilson, Vice President of Investor Relations. Please go ahead.

Good afternoon, and thank you for joining us today. On the call with me are Satya Nadella, Chairman and Chief Executive Officer; Amy Hood, Chief Financial Officer; Alice Jolla, Chief Accounting Officer; and Brian DeFoe, Deputy General Counsel and Corporate Secretary.

On the Microsoft Investor Relations website, you can find our earnings press release and financial summary slide deck, which is intended to supplement our prepared remarks during today's call and provides the reconciliation of differences between GAAP and non-GAAP financial measures. More detailed outlook slide will be available on the Microsoft Investor Relations website where we provide outlook commentary on today's call.

On this call, we will discuss certain non-GAAP items. The non-GAAP financial measures provided should not be considered as a substitute for or superior to the measures of financial performance prepared in accordance with GAAP. They are included as additional clarifying items to aid investors in further understanding the company's third quarter performance in addition to the impact these items and events have on the financial results.

All growth comparisons we make on the call today relate to the corresponding period of last year, unless otherwise noted. We will also provide growth rates in constant currency when available as a framework for assessing how our underlying businesses performed, excluding the effect of foreign currency rate fluctuations. Where growth rates are the same in constant currency, we will refer to the growth rate only.

We will post our prepared remarks to our website immediately following the call until the complete transcript is available. Today's call is being webcast live and recorded. If you ask a question, it will be included in our live transmission, in the transcript and in any future use of the recording. You can replay the call and view the transcript on the Microsoft Investor Relations website.

During this call, we will be making forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could materially differ because of factors discussed in today's earnings press release, in the comments made during this conference call and in the Risk Factors section of our Form 10-K, Forms 10-Q and other reports and filings with the Securities and Exchange Commission. We do not undertake any duty to update any forward-looking statement.

And with that, I'll turn the call over to Satya.

Thank you very much, Jonathan. It was a record third quarter powered by the continued strength of Microsoft Cloud, which exceeded $54 billion in revenue, up 29% year-over-year. Our AI business surpassed $37 billion ARR, up 123%.

We are at the beginning of one of the most consequential platform [ shifts ] that will change the entire tech stack as agents proliferate and become the dominant workload. This will drive TAM expansion and change the value creation equation across the entire economy. To capture this opportunity, we are executing against 2 priorities. First, we are building the world's leading cloud and AI infrastructure for a genetic computing era. Second, we are building high-value agentic systems across core domains such as productivity, coding and security. These 2 layers reinforce each other, and we are focused on driving competitive value and differentiation for customers across each so that they can evalmax their outcomes.

Today, I'll focus my remarks on both priorities, starting with infrastructure. We're optimizing every layer of the tech stack from DC design to silicon to system software, the model architecture as well as its optimization. This is translating into operational gains. We have reduced, [ docked the ] life times for new GPUs in our biggest regions by nearly 20% since the beginning of the year. Our Fairwater data center in Wisconsin came online earlier this month, 6 weeks ahead of schedule, allowing us to recognize revenue earlier. And we delivered a 40% improvement in inference throughput for our most used models across Copilot driven by our software and hardware optimization work.

All up, we added another gigawatt of capacity this quarter and remain on track to double our overall footprint in just 2 years. We are moving aggressively to add capacity aligned to our demand signals we see and we have announced new data center investments across 4 continents. We also continue to modernize our fleet with our first-party innovation alongside the latest from NVIDIA and AMD. Across our fleet, millions of servers are powered by our custom networking security and virtualization silicon, including Azure Boost as well as our first-party CPUs and accelerators. Our Maia 200 AI accelerator, which offers over 30% improved tokens per dollar compared to the latest silicon in our fleet is now live in our Iowa and Arizona data centers. Our Cobalt server CPU is deployed in nearly half of our DC regions running workloads at scale for customers like Databricks, Siemens and Snowflake.

As our largest customers scale their AI deployments, they're increasingly leveraging other services across our platform and choosing to run those workloads on Cobalt, and we are expanding Cobalt supply significantly to meet this demand.

The next [ way ] up from infrastructure is the agent app platform. It starts with model choice. We offer the broadest selection of models of any hyperscaler, so customers can choose the right model for the right workload across OpenAI, Anthropic, open source and more. Over 10,000 customers have used more than one model on Foundry, 5,000 used open source models, and the number who have used Anthropic and OpenAI models increased 2x quarter-over-quarter.

For example, Bayer is using multiple models in Foundry to create its own in-house agent platform with more than 20,000 active monthly users. All up, over 300 customers are on track to process over 1 trillion tokens on Foundry this year, accelerating 30% quarter-over-quarter.

We also remain focused on our first-party model work to differentiate our high-value copilots and agents and reduce COGS. We introduced MAI-Transcribe-1, a state-of-the-art speech-to-texdt model and MAI-Image-2, one of the top image generation models in the world. These models are already powering first-party scenarios like image generation in Bing and PowerPoint, and we are working towards having Transcribe-1-powered transcription in Copilot and Teams. Early signals show 67% increase in GPU efficiency with Transcribe-1 and up to 260% increase in Image-2.

We also brought MAI models to commercial customers like Shutterstock and WPP for the first time through Foundry. And we are innovating on OpenAI IP to drive product evals and lower COGS. Two recent examples of what we have done with multistep retrieval with Work IQ [ and ] Copilot and how reasoning adapts to intent complexity and researcher with much reduced latency and increased accuracy.

The next layer up is all about enterprise data in context across Fabric, Foundry, Microsoft 365 and our [ security graph ], we are building a unified IQ layer for organizational intelligence, thousands of enterprises already are accessing context across these IQ layers. And as AI usage grows, so does the context layer creating a flywheel that continuously improves the grounding, relevance and effectiveness of every agent they use and build making our IQ layers an unmatched context engine for organizational intelligence.

More broadly, our database business accelerated quarter-over-quarter. Cosmos DB alone saw 50% year-over-year revenue growth driven by AI app workloads. We now have 35,000 paid Fabric customers, up 60% year-over-year. And all of the amount of data in Fabric, OneLake data lake increased nearly 4x year-over-year. Over 15,000 customers now use both Foundry and Fabric, up 60% year-over-year as enterprises connect agents to real-time operational analytical and unstructured data that Fabric brings together. And we are very excited about the continued progress with Foundry Agent Service and how customers can now build durable stateful agents that run across time boundaries, orchestrate tools and models and close the loop with evals and improvement over long-running workflow.

Beyond Fabric and Foundry, we are also helping knowledge workers build agents with tools like Copilot Studio. Nearly 90% of the Fortune 500 now have active agents built with our low-code, no-code tools. And we are seeing fast growth of our co-pilot credit consumptive offer, up nearly 2x quarter-over-quarter as customers increasingly extend copilot with custom agents tailered to their workflows.

Finally, with Agent 365, we offer a control plane that extends company's existing governance, identity, security and management frameworks to agents. Tens of thousands of companies are already managing tens of millions of agents in Agent 365, and we expect this momentum to grow significantly as agents will increasingly need tools for identity governance, security and more.

Now let me turn to the high-value agentic systems we ourselves are building on this platform. We are evolving our family of copilots from synchronous assistance to async coworkers that can execute long-running tasks across key domains.

In knowledge work, it was another record quarter for Microsoft 365 Copilot seat ads which increased 250% year-over-year, representing our fastest growth since launch. Quarter-over-quarter, we continue to see acceleration and now have over 20 million Microsoft 365 Copilot paid seats. The number of customers with over 50,000 seats quadrupled year-over-year and Accenture now has over 740,000 seats, our largest Copilot win to date. And Bayer, Johnson & Johnson, Mercedes and Roche all committed to 90,000 or more seats. Copilot is uniquely valuable at work where nearly every task depends on organizational context, work IQ grounds, copilot responses in the full context of an organization, including people, roles, documents and communications, all within the company's security boundary.

The system of work behind Work IQ alone now spans more than 17 exabytes of data growing 35% year-over-year. The liquidity and freshness of that data matters with billions of e-mails, documents, chats, hundreds of millions of Teams meetings and millions of SharePoint sites added each day. And that context is getting even richer as copilot adoption grows, copilot and agent conversations and artifacts that create feedback into Work IQ, making it even more context rich.

We continue to increase the pace of future innovation across Microsoft 365 Copilot introducing over 625 updates over the past year, up 50%. And in Microsoft 365 Copilot, you now have access in chat to multiple models by default with intelligent auto routing in agents with critique and counsel. You can use multiple models together to generate optimal responses. As of last week, Agent Mode is now default experience across Copilot in Word, Excel and PowerPoint. And with Cowork, you now have a new way to delegate and complete work using Copilot. All this innovation is driving record usage intensity across Copilot. We have seen a surge in usage of our first-party agents with monthly active usage up 6x year-to-date. Copilot queries per user were up nearly 20% quarter-over-quarter. To put this momentum in perspective, weekly engagement is now at the same level as Outlook, as more and more users make Copilot a habit.

When it comes to biz apps, we are seeing a new pattern emerge as customers shift from traditional seat model to seats plus consumption. The customer service categories at the forefront of this transformation is nearly 60% of our service customers are already purchasing usage-based credits. For example, HSBC uses prebuilt agents with Dynamics 365 to manage customer inquiries across products, markets, regulatory requirements, reducing issue resolution time by over 30%. And our agentic products in LinkedIn Talent Solutions, which help hirers automate time-consuming tasks like sourcing, screening and drafting messages have already surpassed a $450 million annualized revenue run rate.

When it comes to developers, GitHub itself is seeing unprecedented growth driven by proliferation of agentic coding and we are hard at work to scale and meet this demand. We see this even with GitHub Copilot. Nearly 140,000 organizations now use Github Copilot and enterprise subscribers have nearly tripled year-over-year. The majority of users leverage multiple models. We're also seeing rapid adoption of GitHub Pilot CLI with usage nearly doubling month-over-month. And earlier this week, we announced our move to usage-based pricing model for GitHub Copilot as we align pricing to actual usage and cost.

When it comes to security, the physics of cybersecurity has changed as AI compresses the window between vulnerability and exploitation. To help mitigate risk immediately, we simship Defender protections when updates for AI discovered vulnerabilities are released. And we are on course to productize new multimodal AI-driven scanning harness as well. Already the number of Security Copilot customers increased 2x year-over-year, our data security triage agents alone handled over 2 million unique alerts this quarter, and we are helping customers secure their AI deployments as well. 35 billion Copilot interactions have been audited by Purview to date, up 7x year-over-year.

Finally, when it comes to our consumer business, we are doing the foundational work required to win back fans and strengthen engagement across Windows, Xbox, Bing and Edge. In the near term, we are focused on fundamentals, prioritizing quality and serving our core users better. You see this in the work underway across our consumer products. With Windows, we recently announced performance improvements for lower memory devices, streamline the Windows Update experience and brought back focus to core features and fundamentals that matter most to our customers. And you also see this in Xbox where the team is recommitting to our core fans and players and shaping the future of play. Last week's Game Pass changes are one example of how we are staying responsive to customer feedback. Monthly active Windows devices surpassed 1.6 billion, and over time, Windows value will extend to deliver [ unmeeted ] intelligence at the [ edge ]. Our Edge browser has taken share for 20 consecutive quarters and Bing monthly active users reached $1 billion for the first time. LinkedIn has 1.3 billion members, and we are seeing increased depth of conversation and it's the leading B2B sales and advertising channel for large and small businesses. We set new records for monthly Xbox active users in the quarter as well as game streaming hours. And in Microsoft 365 consumer, we now have nearly 95 million subscribers and early signals show increasing satisfaction as we make Agent Mode the default.

Across everything I've talked about. We're also hard at work changing the way we work. Our North Star remains the same, delivering customer value with highest quality and top-class innovation. And this is what gives me confidence in our ability to shape the next phase of growth for our company and our customers.

With that, let me turn it over to Amy to walk through our financial results and outlook.

Thank you, Satya, and good afternoon, everyone. We delivered results that exceeded expectations across revenue, operating income and earnings per share, driven by strong demand and execution.

As Satya shared, our AI business annual revenue run rate surpassed $37 billion this quarter, growing 123% year-over-year. And we're accelerating our pace of innovation as we execute against the expansive opportunity ahead. This quarter, revenue was $82.9 billion, up 18% and 15% in constant currency. Gross margin dollars increased 16% and 13% in constant currency, while operating income increased 20% and 16% in constant currency. Earnings per share was $4.27, an increase of 21% and 18% in constant currency when adjusted for the impact from our investment in OpenAI. And FX was roughly in line with guidance at the total company level.

Company gross margin percentage was 68% down year-over-year, driven by continued investment in AI infrastructure and growing AI product usage. The impact from these investments was partially offset by ongoing efficiency gains, particularly in Azure and M365 Commercial Cloud. Operating expenses increased 9% and 8% in constant currency, driven by continued investment in AI including R&D compute capacity, talent and data to support product development across the portfolio.

This quarter, growth was impacted by a low prior year comparable, particularly in sales and marketing and G&A expenses. Operating margins increased slightly year-over-year to 46%. Total company headcount declined year-over-year as we focus on building high-performing teams that operate with pace and agility. When adjusted for the impact from our investments in OpenAI, other income and expense was $961 million. Favorability was driven by gains on investments that were partially offset by losses on foreign currency remeasurement.

Capital expenditures were $31.9 billion, down sequentially due to the normal variability from structure buildouts and the timing of delivery of finance leases. And this quarter, roughly 2/3 of our CapEx was for short-lived assets, primarily GPUs and CPUs. The remaining spend was for long-lived assets that will support monetization over the next 15 years and beyond.

This quarter, total finance leases were $4.7 billion and were primarily for large data center sites. And cash paid for PP&E was $30.9 billion, roughly in line with capital expenditures as the impact from finance leases was partially offset by differences between the receipt of goods and payment.

Cash flow from operations was $46.7 billion, up 26%, driven by strong cloud billings and collections, partially offset by an increase in operating lease payments. And free cash flow was $15.8 billion, reflecting higher capital expenditures. And finally, we returned $10.2 billion to shareholders through dividends and share repurchases.

Now to our commercial results. Commercial bookings grew 7% when excluding the impact from OpenAI, driven by consistent execution in our core annuity sales motions. Bookings decreased 4% and 6% in constant currency when including Azure commitments from OpenAI. Commercial remaining performance obligation grew 26%, in line with historic seasonality when excluding OpenAI. RPO increased to $627 billion and was up 99% year-over-year with a weighted average duration of approximately 2.5 years when including OpenAI. Roughly 25% will be recognized in revenue in the next 12 months, up 39% year-over-year. The remaining portion recognized beyond the next 12 months increased 138%.

Microsoft Cloud revenue was $54.5 billion and grew 29% and 25% in constant currency, reflecting strong demand across the Azure platform and our first-party AI applications and services. Microsoft Cloud gross margin percentage was slightly better than expected at 66% and down year-over-year due to continued investments in AI, partially offset by the ongoing efficiency gains noted earlier.

Now to our segment results. Revenue from Productivity and Business Processes was $35 billion and grew 17% and 13% in constant currency. M365 Commercial Cloud revenue increased 19% and 15% in constant currency, ahead of expectations, strong execution and improving product quality drove accelerating M365 Copilot seat adds this quarter, with paid seats now over 20 million. ARPU growth was again led by both E5 and M365 Copilot. And paid M365 Commercial seats grew 6% year-over-year with installed base expansion across all customer segments, though primarily in our small and medium business and frontline worker offerings.

M365 Commercial products revenue increased 1% and decreased 3% in constant currency, down sequentially as Office 2024 transactional purchasing trends continue to normalize as expected. M365 Consumer Cloud revenue increased 33% and 29% in constant currency, again driven by ARPU growth. M365 consumer subscriptions grew 7%. LinkedIn revenue increased 12% and 9% in constant currency, with growth across all lines of business. Dynamics 365 revenue increased 22% and 17% in constant currency with continued share gains and growth across all workloads.

Bookings growth was impacted by weaker renewals as customers balance spend between the traditional per seat and the emerging seats consumption model. Segment gross margin dollars increased 18% and 13% in constant currency, and gross margin percentage increased slightly, again, driven by efficiency gains in M365 Commercial Cloud that were partially offset by continued investments in AI, including the impact of growing adoption and usage of Copilot.

[ I guess ] the low prior year comparable, operating expenses increased 11% and 9% in constant currency, driven by the shared R&D AI investments mentioned earlier as well as higher co-pilot advertising spend. Operating income increased 21% and 14% in constant currency, and operating margins increased year-over-year to 60%.

Next, the Intelligent Cloud segment. Revenue was $34.7 billion and grew 30% and 28% in constant currency. In Azure and other Cloud Services, revenue grew 40% and 39% in constant currency against a prior year that included accelerating growth. Results were ahead of expectations as we delivered capacity earlier in the quarter, enabling increased consumption across both AI and non-AI services. Strong customer demand across workloads, customer segments and geographic regions continues to exceed available capacity.

In our on-premises server business, revenue increased slightly and decreased 3% in constant currency with ongoing customer shift to cloud offerings. Segment gross margin dollars increased 19% and 18% in constant currency. Gross margin percentage decreased year-over-year driven by continued AI investment and increased GitHub Copilot usage, partially offset by ongoing efficiency gains in Azure. Operating expenses increased 9% and 7% in constant currency, driven by the shared R&D AI investment noted earlier. Operating income grew 24% and 23% in constant currency, and operating margins were 40%.

Now to More Personal Computing. Revenue was $13.2 billion and declined 1% and 3% in constant currency. Windows OEM and Devices revenue decreased 2% and 3% in constant currency. Windows OEM increased slightly and was ahead of expectations as OEM and channel partners continued to build inventory given increasing memory prices. Search advertising revenue ex TAC increased 12% and 9% in constant currency, was both driven by higher volume and revenue per search across edge and. And in gaming, revenue decreased 7% and 9% in constant currency. Xbox content and services revenue decreased 5% and 7% in constant currency against the prior year comparable that benefited from strong first-party content performance.

Segment gross margin dollars increased 6% and 4% in constant currency and gross margin percentage increased year-over-year driven by sales mix shift to higher-margin businesses. [ I guess ] the low prior year comparable, operating expenses increased 7% and 6% in constant currency driven by impairment and other related expenses in our gaming business as well as continued investments in shared R&D mentioned earlier that benefits the entire portfolio. Operating income increased 4% and 1% in constant currency, and operating margins increased year-over-year to 28%.

Now moving to our Q4 outlook, which unless specifically noted otherwise, is on a U.S. dollar basis. Based on current rates, we expect FX to increase revenue growth by roughly 1 point in Productivity and Business Processes and More Personal Computing with no meaningful impact to Intelligent Cloud. Overall impact to total revenue is expected to be less than 1 point. And FX should increase COGS growth by roughly 1 point with no impact to operating expense growth.

Starting with our commercial business. In commercial bookings, when adjusted for the impact from OpenAI, we expect healthy growth on a growing expiry base with consistent execution in our core annuity sales motions against a significant prior year comparable. Microsoft Cloud gross margin percentage should be roughly 64%, down year-over-year, driven by continued investments in AI and increased GitHub Copilot usage. Just this week, we announced a business model transition in GitHub Copilot that will align pricing with usage and value that takes effect on June 1 of this year.

Now to segment guidance. In Productivity and Business Processes, we expect revenue of USD 37 billion to USD 37.3 billion, a growth of 12% to 13%. In M365 Commercial Cloud, on an adjusted basis, we expect revenue growth to be between 15% and 16% in constant currency. When normalized for the prior year comparable that benefited from 2 points of in-period revenue recognition. And on a reported basis, we expect revenue growth to be between 13% and 14% in constant currency.

Building on the Copilot momentum we saw in Q3, we expect net paid seat adds to increase sequentially, which will drive continued ARPU growth. M365 Commercial products revenue should grow in the mid-single digits against the prior year that benefited from higher-than-expected Office 2024 transactional purchasing. As a reminder, M365 Commercial products includes components that can be variable due to in-period revenue recognition dynamics. M365 Consumer Cloud revenue growth should be in the low 20% range, down sequentially as we start to lap the benefit from last year's price increase. Growth will again be driven by ARPU and an increase in subscription volume. For LinkedIn, we expect revenue growth of approximately 10%. And in Dynamics 365, we expect revenue growth to be in the low double digits, down sequentially with impact from a strong prior year comparable and the bookings trends noted earlier.

For Intelligent Cloud, we expect revenue of USD 37.95 billion to USD 38.25 billion or growth of 27% to 28%. In Azure, we continue to focus on accelerating the delivery of capacity and increasing fleet efficiencies, and therefore, we expect Q4 revenue growth to be between 39% and 40% in constant currency against a strong prior year comparable that included accelerating growth. Broad and growing customer demand continues to exceed supply, and we continue to balance the incoming supply we can allocate here against our other high ROI priorities, first-party applications, R&D and end-of-life server replacement.

As a reminder, year-over-year Azure growth rates can vary quarter-to-quarter based on capacity, timing and contract mix.

In our on-premise server business, we expect revenue to decline in the mid-single digits with ongoing customer shift to cloud offerings.

In More Personal Computing, we are lapping strong prior year comparables, navigating complex PC market dynamics impacted by memory prices and refocusing on delivering quality and value to consumers. Therefore, we expect revenue to be USD 11.75 billion to USD 12.25 billion. Windows OEM revenue should decline in the high teens with roughly 6 points of impact from a prior year comparable that benefited from Windows 10 end of support, 6 points from inventory levels that we expect to come down for the quarter and 6 points from a lower PC market as prices increase due to memory cost. The range of potential outcomes remains wider than normal. Therefore, Windows OEM and Devices revenue should decline in the mid- to high teens. Search advertising revenue ex TAC growth should be in the high single digits, driven by revenue per search and volume with continued share gains across Bing and Edge. And in Xbox content and services, we expect revenue to decline in the low teens, reflecting a prior year comparable that benefited from strong first-party content as well as the recent price changes for Xbox Game Pass as we focus on delivering more value to gamers. Hardware revenue should decline year-over-year. Therefore, at the total company level, revenue should be between USD 86.7 billion and USD 87.8 billion or growth of 13% to 15%, with accelerating commercial growth, partially offset by our consumer business.

Our Q4 outlook for COGS and operating expenses includes roughly $900 million in onetime costs for the recently announced voluntary retirement program. Therefore, we expect COGS of USD 29.4 billion to USD 29.6 billion or growth of 22% to 23%, including roughly $350 million from the retirement program. And operating expense of USD 19.3 billion to USD 19.4 billion or growth of approximately 7%, including roughly $550 million from the retirement program.

Even as we invested through the year, [ and ] additional capacity to serve the growing AI platform, apps and services demand and inclusive of these onetime costs, we expect full year FY '26 operating margins to be up about 1 point year-over-year. Excluding any impact from our investments in OpenAI, other income and expense is expected to be roughly negative $100 million as interest income will be more than offset by interest expense, which includes the interest payments related to data center, finance leases. And we expect our adjusted Q4 effective tax rate to be approximately 19%.

Next, capital expenditures. We expect CapEx spend to increase to over $40 billion as we continue to bring more capacity online. The sequential increase includes roughly $5 billion from higher component pricing as well as the impact from finance leases, which add variability given the full value is recorded in the period of lease commencement. And we expect the mix of short-lived assets to remain similar to Q3.

For calendar year 2026, we expect to invest roughly $190 billion in capital expenditures, which includes approximately $25 billion from the impact of higher component pricing. We remain confident in the return on these investments, given higher demand signals and increasing product usage as well as the efficiencies we're already driving across the platform. Even with these additional investments and continued efforts to bring GPU, CPU and storage capacity online faster, we expect to remain constrained at least through 2026. Despite these constraints, and the continued need to balance incoming supply. We expect Azure growth to show modest acceleration in the second half of the calendar year compared with the first half.

Now I'd like to share some closing thoughts as we look to next fiscal year. First, we continue to evolve how we operate to increase our pace and agility. And therefore, we expect headcount will decrease year-over-year. Operating expense growth will be in the mid- to high single digits, reflecting ongoing investments in R&D, inclusive of AI investment in compute, data and talent to accelerate product innovation.

Next, as a reminder, we will lap strong prior year comparables impacted by Windows 10 end of support, elevated OEM inventory levels as well as increased office and server transactional purchasing. And finally, we remain focused on delivering a platform that enables customers to build and run AI solutions and on driving innovation in our first-party AI applications and services. And therefore, we expect another year of double-digit revenue and operating income growth in FY '27.

In closing, we are committed to delivering innovation that helps customers create new business value as we enter the final quarter of our fiscal year.

With that, let's go to Q&A, Jonathan.

Thanks, Amy. We'll now move over to Q&A. [Operator Instructions] Operator, can you please repeat your instructions?

[Operator Instructions] And our first question comes from the line of Keith Weiss with Morgan Stanley.

Congratulations on another really solid quarter. The Microsoft 365 Copilot numbers, super impressive, I think, way ahead of most people's expectations. I wanted to ask a broader question on demand. We've strong demand for a while. We see it in our CIO surveys and you guys definitely express it in what you're seeing in your business.

Maybe in the short term, you could talk to us about how that demand translates into commercial bookings and how that might be changing. You mentioned different contracting cycles between seats and consumption that may impact that. And then we also have to think about renewal basis. And then longer term, and maybe this opens it up to Satya, what is supporting this demand over time? Or is that another way, who is paying for all of this? Because while we see excitement for Microsoft in our CIO survey, like our overall IT spending expectations aren't increasing and GDP growth isn't really increasing. So at some point, like how does this get paid for? And you start to see the indications of where those dollars are going to come from?

You want to start...

Why don't I start with the first half of your question, Keith, around how these models impact bookings. And I think it's really important. You're right. You have the normal cyclical things that happen with bookings, it's the exploration [ phase ] or maybe large multiyear Azure commitments to get signed. And that stuff has always had some volatility to it. But I think if you take a step back, which is I think the broader question you're asking, and obviously, I'll let Satya talk to it, too, you're really thinking through as we go through using a model that's historically thought of as a per seat business. And suddenly, if you think about getting worked on and being more productive, it's thinking about being a seat or a worker plus an agent.

And when I think about that model, I start to think about it as a license business plus a consumption business and really applying far more broadly than I think people have thought about that. And so it starts to mean that over time, bookings will actually also look a little different. It will still have that per seat license logic, but it will also have a meter, just like you see in Azure. And it may not all flow through bookings in the same way. You'll just bill for usage. And if that usage has great value to customers, and I'll let Satya talk a little bit about this, then you'll keep spinning and keep using those agents if they're adding direct value or growth to your business. And so I think it's probably healthy to sort of start to think about that transition in a broader way. Well, you may not see it in the short term in bookings. I think if I were to frame how to think about the opportunity, I would probably think about it more in that light.

Yes, I think Amy captured it. I think the basic transformation of call it, say, any per user business of ours, whether it's productivity, coding, security will become a per user and usage business. That's the best way to think about. It's obviously already happening with coding. That's where you see it already perhaps at scale. Some of the business model changes even we made this quarter speak to that.

But it also speaks, I think, to the intensity of usage, right? Because where is these dollars going to come from. At the end of the day, it is going to come from some eval and outcome that a business has, where these agents that are working on behalf of users or with users is creating value. And so that's sort of where it starts, whether it's customer service, whether it's individual productivity, team productivity, a business process. Some costs [ per ] is either decreasing because of the use of agents or some revenue is increasing because of agents because it was able to compress these workflows.

And that's what you broadly start to seeing, right? Even when people talk about Copilot, it's obviously, they use chat, chat with reasoning, they use Cowork, they use Agent Mode inside a Word, Excel, PowerPoint, but it's all been done in the context of some task trajectory. And so when they start seeing that the trash trajectory is compressing the workflow, improving revenue, decreasing costs, that is what's driving usage. So it may not be, by the way, pure seat coverage type of motions like in the past. This is more about getting intense users and intense usage. And that's what we're focused on.

And Keith, maybe just to take a quick second. Just a big thank you to you. It's been a real privilege to work with you over many, many quarters. And just to say, we really appreciated your coverage over this time. And congratulations, [ making use your ] last earnings call.

And thank you so much, Keith. And [indiscernible] you.

The next question comes from the line of Karl Keirstead with UBS.

Okay. Great. Amy, could you elaborate a little bit on the CapEx guidance you just provided. Obviously, it requires a fairly material pickup in CapEx in the second half of the calendar year, maybe to the tune of $120 billion. I'm just curious your confidence in working through the physical component constraints to hit that number does it involve the greater use of partners? And how are you thinking about allocating that increased capacity between third-party and first-party, do you have a general framework you'd advise us to keep in mind?

Sure. Thanks, Karl. No, I actually -- I feel quite good about our ability to work through the physical sort of limitations. I think of the industrial logic of the supply chain, to be able to put that both -- some of that, as we've talked about, is getting capacity online, but a lot of that is far more short term in nature, being able to get CPUs, GPUs, storage put in place to be able to start to support better the demand signals we've been seeing. Trying to give some help on part of that being priced. I think that just helps give you a sense on volumes. And obviously, it leads more to short-term assets when you see that type of impact of price on the number.

I would also say, in terms of a sense of allocation, you should assume -- we talked a little bit about what you were seeing in Azure looking for 39% to 40% in constant currency in Q4 means that we're able to use some efficiencies to make sure we're able to meet demand as we can best do that in a balanced way across Azure, our Copilot usage, which I think you've seen in Q3 has really been on a different trajectory than we saw it up to this point that applies across coding. It applies across productivity, and I have some confidence it's also going to apply across security.

Then if you think about talking about some acceleration into what I would call the first half of calendar -- the first half of FY '27, the second half of the calendar year, it means we're getting some insights to our abilities to increasingly put pressure on efficiencies being able to speed up the deliveries into our data centers and make that what I would call revenue ready as quickly as we can. So I would expect the pressure between first-party usage and being able to meet Azure demand will persist as I said, but we're doing our best to be able to get things in as quickly as we can and hence, the CapEx number that we see in the second half of the year.

The next question comes from the line of Brent Thill with Jefferies.

One of the big pushbacks we all get is that AI is going to be really expensive. Yet you, Google and Amazon are showing higher margins tonight as you report. What are investors missing? And why is AI a potential better margin for the industry over time?

Thanks, Brent. I think -- we've been talking about sort of where this AI business of ours has been in the cycle compared to even the cycle we saw with the cloud, which now seems very long ago. And how margins were actually better and they remained better in our AI business versus where we saw in the cloud transition, looking back. And so I do feel like what we've been really focused on is making sure that the business models reflect how these applications are both getting built and the value that they're bringing. And so when you think about that type of value. It tends to be captured more and consumption and usage-based pricing models. And I think that's something that's probably been a little underappreciated as we look in terms of margins going forward.

I also think it's been important to us to make sure we leverage the IP we have the IP we get from our partnerships is obviously free to us for a long time. So we're able to take that and apply it and to benefit our margins in a healthy way. You've also seen us work hard on the first-party hardware stack being able to make sure we can take margins out of the infra stack as well. And then, of course, just the efficiency work. We've really been in an accelerated phase, as you know, of trying to get as much capacity as we can get into production. But when you go through that, you also can start to focus on the efficiency work. That's efficiency work on the hardware side as well as efficiency work on the software side to be able to deliver these types of margins.

I do believe that one of the real focuses that we've got to all have is that -- and this really dates back to the question that Keith asked in the beginning, which is that when you move to usage-based models, you have to make sure you're delivering incredibly high value to customers. So what we need to do is make sure the focus starts with customer usage that creates value. If that creates value and positive output than the TAM expansion here and the ROI will be very good.

The next question comes from the line of Mark Moerdler with Bernstein Research.

Congratulations on the quarter you delivered and the rate of growth and some of the commentary you've made on guidance, et cetera. What I'd like to drill in a little bit is on this whole question of of the CapEx and the spending that you're making. Obviously, the Commercial Cloud is growing fast. Azure is growing fast. AI is growing even faster within your overall business, but there's a bit of a disconnect that makes investors a bit nervous between how fast they're seeing CapEx growing and how fast they're seeing revenue growing. So can you give some color about how the timing works out, or how much needs to be spent on replacement of equipment or first party in order to build that confidence that as we look towards the strong spending on CapEx, that the core business will continue to be very, very healthy and that the margins will be good.

Thanks, Mark. Let me maybe start with Azure, which given its size and its growth rates where we talked about acceleration from where we are, which is the guide of 39% to 40% into a bigger number in the second half of the calendar year.

When you start to see that type of growth rate on the size of the business we have, the amount of spend being done on short-term assets, which is really the thing that correlates with revenue as opposed to these -- the 1/3 of that number-ish, that's going into 15-year assets or some lumpy timing from lease contracts that can kind of get confusing. I think in so many ways, this just reminds us of the last cycle. And when the TAM is so expensive and when shortages are generally, I think, growing seems to be the sentiment between supply and demand. It just gives you a lot of confidence in the ROI on certainly and starting with the platform side.

I think what you're really asking is whether as we see these usage plus consumption models emerge at the app and services layer, are we starting to see the benefits of that. And I think if you look past the last quarter, I think we saw some acceleration, which I felt good about in the M365 Commercial Cloud number this quarter. We're guiding for that to be better again in Q4. I think that's where you're starting to see, right? I think the thing that investors have been asking and Mark are asking about is we'll start to see that show up in revenue growth. And I think that's the first place you point to. We can also point to it, and I think you'll start to see it in in GitHub, right, where you see revenue growth rates and usage consumption models result in acceleration in the top line. And then in general, I think we continue to see that right?

And so when you think about spending that amount of capital, putting it into production, seeing some delay before it turns into revenue ready, having the book of business, I think it's over $600 billion of revenue that we still need to deliver, and that's before we're starting to see the acceleration in seats that we're seeing on Copilot, I do feel very good about, frankly, that number. And our real focus will be how much of that we could pull in as fast as we can. I just want to be transparent but when you have revenue that's sitting there that can be growing faster or efficiencies, the focus needs to be on doing that and landing this CapEx as quickly as we can and converting it to revenue as quickly as we can.

I mean I'm going to just add one point here, which is I think at some level, one of the things that we have learned even in the last, whatever, 2 years or so in AI and also build more conviction and confidence on is where is the TAM and the category economics of the TAM. And so this, I mean, it's fascinating that here we are in 2026 and the most exciting things are plug-ins in Word or Excel or CLIs in coding or -- and so when you see that, that means we have a structural position in knowledge work, coding, security, which are the big caps. And then you couple that with the right business model, which is what Amy was referencing multiple times, which is user plus usage. And then you take even the book of business we have, that sort of is a true line -- and if anything, we want to make sure we are getting the CapEx to get the capacity in time for those increases in usage, which I think is going to be very, very key.

And you got to remember the model capabilities are exponential. So we [ should ] think about even Agent Mode in Excel, it sort of kind of didn't work until it started working. And that's just because the model showed up. And so you have to be ready for those opportunities.

That's extremely helpful. I really do appreciate it. And again, congratulations on the quarter.

The next question comes from the line of Gabriela Borges with Goldman Sachs.

Satya, I would love to hear some of your reflections on the Copilot given the technical and commercial milestones that Microsoft just hit just in the last 3 months, so maybe share with us a little on your learnings from Copilot adoption to date. What do you think is working? What's not working? How is that now informing your E7 strategy and your Copilot Cowork strategy?

Thank you for the question. So I think the way to perhaps think about even Copilot, is the Microsoft 365 Copilot and knowledge work in some sense this pattern. We've learned a lot, as I said, even from coding. But if you sort of focus it on M365 Copilot, the first thing is to think about even the form factor and the shape of the product and how it's evolved, right? So there's chat, chat now with reasoning [ over ] Work IQ. So that's sort of one form factor. Then there is all the agents like researcher and analysts that you use within chat, or even custom agents that our customers are building. And then on top of that, you now have this [ edit more ], right? So if you think about it typical trajectory or a session in Copilot as it starts with chat, you do -- you ask some questions, you get some insights, you ask it to even generate an artifact, you open that artifact in Word, Excel, PowerPoint, you further refine it. So in other words, you continue the conversation.

And then, of course, we now have even a complete new form factor where you essentially delegate the task, right? So you're not even interactively working but you're delegating the task with Cowork. So these are all the various form factors. And one of the most interesting things to keep in mind is the usage of this is at the same level as Outlook. So this is not -- even to the previous question, are people using it, finding it useful. I mean this is like a daily habit of intense usage.

The other thing that is important when you think about what makes these form factors useful is intelligence and the intelligence is a function of 2 things. It's an intelligence of having multiple models coupled to context, that's the meetings, the documents, the e-mails, the teams, I mean all of that SharePoint data, all of that rich by the way, constantly updated, right? So this is not some static database. It's the most important database in any company that is constantly changing every second. So that's the context, the models brought together with the harness that's multi-model. This is essentially the same thing we do in GitHub, whether we do it in M365, we do this in security, which is our core goal is to decouple the harness from the models and then have the context richness show through because customers are going to use multiple models.

In fact, if you look at critique or console, that's a great example, or Rubber Duck in GitHub Copilot. These are good examples of why you want or even in Excel, I generate using Opus and I check with Codex. That's the type of things that you want users to have access to. And so that's really the -- and then couple that with the business model of user usage plus user pricing, I feel like that's what's happening, and we are seeing that all play out.

The next question comes from the line of Kirk Materne with Evercore ISI.

Amy, I was wondering, can you just talk a little bit about the change in the OpenAI agreement, if there's anything we should be aware of from a modeling perspective or from a financial perspective, that would would change today versus where we were maybe a couple of weeks ago? And then I guess, Satya, for you, it seems like an opportunity for you guys to continue to diversify from a model perspective. Any other takeaways we should be thinking in terms of where you guys landed with OpenAI and new framework.

Yes. Maybe I'll start. I mean, overall, we feel good about partnership with OpenAI, I'm always [ fairway ] focused on any partnership and ensuring that there's a win-win construct at all times. I mean that's how you can remain with partners. In this case, it starts with, quite frankly, IP, Amy referenced this. We have a frontier model. royalty-free with all the IP rights that we will have access to all the way to 32 and we fully plan to exploit it. And there are examples I talked about [ in you ] in my remarks earlier, and that's we are thankful for that, and that's sort of one part of the agreement.

The second part, of course, is the them as a customer of ours, they're a large customer of ours, not just on the AI accelerator side, but also on all the other compute side, and so we want to serve them well. And then, of course, we have our equity. And so overall, I think the construct as they have grown and we have grown and our customers also have different expectations in terms of their model diversity. So therefore, we've all revolved the partnership, but I feel very good about where we are.

Yes. I think the only maybe 2 things to keep in mind. I would say is having the revenue share exists through 2030 and the predictability of that is a real positive for us. And then as you put out, that Satya pointed out, the thinking about that as royalty free with the elimination of our rev share to them.

Thanks, Kirk. Operator, we have time for one last question.

The last question will come from the line of Rishi Jaluria with RBC Capital Markets.

I wanted to go back the discussion we've been having today on seat-based models and consumption. And maybe kind of the philosophy for how this changes over time. We'll get a complete agreement with what you're seeing out there and totally makes sense. Maybe I want to drill into -- you announced E7, which will come out. That is predominantly seat-based with some consumption components. You're doubling down on that seat element. And it seems increasingly customers still want the predictability of seat-based models, as we see with all the kind of usage issues that companies are running to as AI has kind of gone out of control. Can you maybe understand how to bring all these pieces together how to maintain predictability within the customer base while increasingly growing consumption, and maybe if we were to fast forward 3, 5 years in the future, what we -- how should we be thinking about that? What that mix of consumption versus traditional seat base looks like?

Yes. I mean at a high level, maybe we should add to it. But I think you said it, which is, customers want predictability, especially for budgets and procurement. And the seat-based pricing is just entitlements to some consumption right. And so that's, I think, the way to think about it, which is there is some base usage rights that get bundled in or packaged in to see it's a convenient way for people to buy some essentially consumption packs that happen to be assigned to see agents.

And then beyond a certain level, there's overages go into pure consumption. And even there, if you have commitments, long-term commitments to consumption, you get discounting that is appropriate with it. So I feel like that's the direction of travel. And then -- the other thing you mentioned is how is this going to -- from a customer perspective, they're more evaluated by evals. Where are they seeing the value of tokens, as simple as that. So where they see the outcome, the eval and the token, whether it's improving revenue, improving efficiency, and that's what will define.

Like when we talk about IT budgets, IT budgets are going to have to be reshaped by a combination of business outcomes, making their way into into IT budgets and maybe reallocation from other line items on the income statement like OpEx.

Thanks, Rishi. That wraps up the Q&A portion of today's earnings call. Thank you for joining us today, and we look forward to speaking with all of you soon.

Thank you.

Thank you.

This concludes today's conference. You may disconnect your lines at this time, and enjoy the rest of your day.

Excellent. Thank you, everyone, for joining. I'm Keith Weiss, I run the software equity research franchise in the U.S. here at Morgan Stanley. And really thrilled to have with us from Microsoft, Satya Nadella, Chairman and CEO. Satya, thanks so much for joining us.

Thank you so much. It's thrilled to be here.

Excellent. So it's really an amazing time to be talking to you about what's going on in software, partly because of how much innovation is going on in software right now, partly because of how much uncertainty there is in this room amongst investors, and we're seeing it in the stock market. And I'd say over the past several weeks, we've seen even renewed pressure, even more uncertainty about real fundamental questions on how software is going to be built, how it's going to be delivered, how it's going to be priced on a go-forward basis. So maybe we could start there and get your perspective. What's your view on how software is fundamentally going to change over the next 3 years, over the next 5 years?

Yes. I mean it's fascinating, right? Here we are in 2026. And you sit around and you say, God, there are start-ups that are getting founded and funded in massive amounts. If you're an Excel plug-in, you are a VS Code fork, people are in love with file systems again. I mean yesterday, there was a tweet which I loved, which is somebody said, oh, instead of just using grep, I can even do an index now. And I mean like this is mind-blowing in the sense that something is happening where we are rediscovering some of the oldest things we had, like CLIs and IDEs and Excel plug-ins. So it's a good question. Like what has changed? I think to me, I go back to what is this AI thing all about, right? It's a pretty powerful capability we now have to essentially predict trajectories, right?

That's what AI does. AI sort of takes what is, call it, knowledge work or a consumer sort of task, right? It could be a shopping task or a research task or I'm trying to create a sophisticated Excel model or I'm trying to do a forecast. Those are just basically knowledge work, their trajectories and AI is just great at predicting them. So a couple of things are happening as you put these models as they get better and better into our work and into our workflow. The first thing that's happening is the velocity is going up. If you think about -- there's all the stats, right, which is Dario, I think, was here last night, and he talked about sort of what's happening in coding. And you see that even what he's seeing in -- with Claude, we see it in GitHub in the sense the repos are sort of skyrocketing, right, where the public repos have what, 4%, 5% are all basically generated by these coding agents. And the same equivalent is happening in office. I mean it's crazy. I mean our SharePoint, OneDrive, I mean, the artifact creation has become the thing that everybody does.

And because it's cheaper, it's so much easier to create sophisticated. And it's not just bad artifacts. These are sophisticated artifacts that are getting created as part of a trajectory, as part of a task, as part of a workflow. And so in fact, last night, I was playing with this where I wanted to track all start-ups and said this is funding in start-ups. So I put up -- we have tasks now in Copilot that's in research preview. Think of it as our coworker. And it created this fantastic spreadsheet, right? It went off, did a bunch of work.

And the spreadsheet -- the problem -- so what it did is it generated an unbelievable spreadsheet at the end of a long-running sort of horizon task. Then it was great because I handed my cognitive load to it, but then it transferred it all the way back to me by creating an artifact and said, now go, here is the output, go make sense of it. So this back and forth between human and AI is going to be the next thing, which is AI is going to be great at doing cognitive work because it offloads cognitive work. But then it also is great, like a GitHub repo that was sort of completely white coded, what the heck are you going to do with it?

I mean you got to then go again, apply AI to understand it. So the same thing is happening even in office and in knowledge work. So what I did, in fact, was go into Excel agent mode and use it essentially as a new linter, new reasoner, a new profiler because that's kind of what Excel does, right, which is it allows you to make sense of -- sort of my tool to reason over sophisticated outputs. In the past, only humans created it. Now humans and AI create it. And so that is sort of what's happening. And the one other thing, Keith, to your point, which is also fascinating, the network effects of intelligence is how I think of it.

Think about -- I'm sitting in front of my CLI and GitHub and coding away and I say, God, we have all these design meetings that are going on. I updated -- I know one of my colleagues updated sort of a spec. It's in SharePoint. I want to make sure that the code that is in my repo is in sync with all of that, right? Literally, meetings and some document. I can go to Work IQ, which is the database underneath M365, right?

In fact, I always said that's the most important database in the company that is least used as a database because they use it as just a transactional store. But now you can. I can load it up as an MCP server and GitHub. And I can say, please go check all my meeting notes, transcripts and SharePoint documents and make sure that the repo is in sync. And literally, it goes off and creates a plan and then starts executing a plan.

That unlock of intelligence or the network effects of intelligence. In fact, I'd like to say that the next office, which is bigger than all of the office that came before, may be headless. And that is sort of going to be the TAM expansive part for us.

Right. Let's dig into that TAM expansive part and maybe focusing on Microsoft 365 and that franchise. It's always been a core franchise for Microsoft. You're talking about the increase in volume in Artifacts and GitHub. One of the other things we've seen is an increase in subscriptions. I think over 2025, we're over a 20% increase in subscriptions. So the users are increasing as well. And Microsoft, particularly Microsoft 365 has always been about enabling those users. So when you're thinking about this Agentic future, is it more users? Or is it just more agents? And how does Microsoft make sure that they stay in front of that curve?

Yes. I mean I think -- remember, subscriptions are just entitlements because it's easier to budget, right? Like why doesn't everybody just do consumption? The reason why everybody doesn't do consumption is sometimes it's easier to forecast. In fact, we are running into this now, right? With coding agents, we're running into it, which is because you kind of have this effect of sort of -- there are people who are using lots of tokens. There are people who are using what I'll call normal sort of volume of tokens.

And so that by modality or the distribution is not yet stable. And so I think what's going to happen there, first, even for just users because there's -- if you think about there are 3 types of modes, right? There's chat, let's call it, what we will call tasks or cowork, right? It's delegated access. So I give something else, which is a synchronous, my credentials and say, please work on my behalf. So that's the second thing.

And the third would be a full digital worker, right? So which is a -- it will have its own identity. It will have its own tools, it will have its own desktop, whatever, right? That sort of is the third thing. And so if you take all these 3, I think the combination of subscriptions with some limits of usage plus a meter is where I think we will end up. And it doesn't matter if it's a person or an agent. And I think the first place where this is happening already with the business models is in coding. And I think information work also will happen. So from a TAM expansive perspective, for us, I look at all agents as users and with maybe more flexibility on how people license that.

Got it. Got it. And if we think about sort of the conversation of evolutionary versus revolutionary, when I look at a lot of the dynamics of Microsoft 365 Copilot, but also what we're seeing more broadly in the environment and what the AIs are trying to do with their generic computing vision, it feels to me like an extension of what we've been talking about for a decade with Power Automate and Power Flow and Power BI of just bringing more visibility in terms of your systems, your data, who you're communicating with into one place. So do you see Copilot really emerging as a new AI-powered user interface for all that, for the systems, for the data, for your teammates for your communication?

Yes. I mean the interesting thing on this design side as to what happens to UI in general or what have you is -- the dream was always the case that can you have some form of a universal interface to all of computing. I mean that's kind of how operating systems evolved. The browser came next and so on. And so this -- the power of natural language and the access to all of the tools with an agentic loop, I think, makes it very powerful.

The question is, if you look at even a model company, right, it sort of -- they started with chat and now there's multiple apps. So everybody has to -- this is where the power of UI, right? I mean what did OpenAI do with Codex? They have a Codex app. and makes sense, right? Because when you have something that is so strong in the sense of usage, then having a heads down usage app for that makes sense. But the workflow between this is what you have to manage. Like for example, even in the thing that I posted this morning was I kind of generate -- I assigned a task, it generated the spreadsheet.

I actually use the spreadsheet right on Canvas inside of "task interface, right, inside of Copilot. I even have fantastic edit capability. By the way, this has been going on forever, right, which is how does Google Docs work versus Office? I mean it has an IR, an intermediate format. So basically, you kind of take office docs and you have an intermediate format in which you manipulate. So all these things will have some intermediate format in which you will work.

And then ultimately, you can export out back to the full UI if you want to, so that you can go deep. And the other big thing is sharing, right? One thing, a lot of AI still is not yet fully cracked. In fact, you see this in coding. We've not fully cracked multiuser. So the next phase is inside of Teams for us, how do you crack agent to agent to human inside a channel. And that's where UI will matter because even if AIs are all doing their work, I as a human need to reason about it. I need the UI that I use. And that, I think, is where it will all come down to.

Oh, by the way, the one other thing I should mention is we are looking at -- like I was looking at why are we spending so many tokens on all this, right? Sometimes it's kind of the most token inefficient things, right? The smart thing that I think everybody is going to start doing in the next year is tools use. That's sort of all software. And that is the way you make AI token efficient. So don't think of this as like tools. Think of it as even if you're all AI pilled, go and say, oh my God, I need a lot more software to make my AI efficient. That's another way this new world will be born.

Got it. Can we talk about sort of with Microsoft 365 Copilot and the -- getting this into your customer base. You have 450 million information workers that are using the suite. Investors got concerned by some demos that we saw from Claude with Claude Cowork. And for the first time in really the last, I would say, definitely 5 years, maybe a decade, there's more concern about the positioning that Microsoft has with Copilot.

But then on the other side of the equation, when I look at my CIO surveys, 80% of the CIOs say that they're either using Copilot today or expect to use in the next 12 months. So CIOs are seeing something in Copilot that I think investors are missing. Can you help us bridge that gap? Like what are the CIOs looking for from Copilot that investors aren't seeing?

Yes. I mean I think, first of all, it's great to see, again, this is where the model capability and also these form factors, right? The -- and this happened even in coding, right, which is get up Copilot, pave the way. And then now we have, quite frankly, a lot of good competition. But the market is kind of massive, right? It's no longer the tools business that I grew up with for the last, whatever, 40 years, and this is a very different market, which is 100x what we were competing for.

And so the form factor innovation that's happening, right, whether it's Chat or Now Cowork or Worker, that's sort of the direction. So for us, we want to do what we have always done, right, which is what we did even in the previous era, which is we have our permission in especially commercial customers through IT. And so we want to love our IT customers because they are the ones who care about lots of things. They care about security, they care about compliance. They care about all of the observability, right?

I mean the reality is come on. I mean I can't launch open claw as Microsoft. I mean, it just wouldn't work. I don't have permission to do that because that would be considered Microsoft launching a virus. I mean that's just not a thing. And so what we have to do -- but at the same time, it's a fantastic innovation, right? That's where -- I mean, the fact is think about like what the last 15-year journey in enterprise was. They didn't want local file systems. And this is about exposing a full-on local file system.

I mean, Morgan Stanley, if I go to [ 10 ] and say, hey, I have news for you, like please I launch something that where I want all the state and the local file system. I mean you'll keep me out of the room. So the thing that we have to be mindful of is really know where our permission comes from and also know what the expectations of our customers are and do a fantastic job of putting the form factors that people love. That's kind of one -- that's the path we are on.

And if you -- to your point, what's happened is the unlock of Work IQ, which is, again, the database underneath M365. So you were showing me the one note. I mean, think about it, right? I prepped for a meeting. I just go in and say, hey, I'm meeting Keith, can you prep my meeting brief, right? It will literally know everything from 2014 to now, every meeting I've had with you, everything you said and which our folks noted and reason over it, not like a search, right? It's a state full agent.

In fact, you can think of WorkIQ as our frontier model, right? It's basically the data plus the model embedded together. That's what the CIOs see the value, right? It's sort of compounding. When I say network effects of intelligence, that's a massive value for them, which is they've invested for 15 years on all of this, and now you can even compound the value.

Got it. It's a good segue into talking about the model layer. And there's definitely a lot of debate in the industry about the AI model layer and whether it's going to concentrate to a small number of frontier models that are just going to get bigger and better over time or whether we should be thinking about a broader set of models, more SaaS-specific models, to open source models. So how do you think about that trade-off between is this market going to consolidate? Or should we be thinking about an ecosystem of models?

I think there are a couple of things. One is, I think at this point, it's fairly clear industry structure-wise when it comes to the narrow way we talk about frontier models, like the American ones are closed, a lot of the Chinese are open. That, I think, is going to be multi-model, right? So there is not going to be a company or a product company other than the model companies, of course, are going to be homogenous, right?

Any one of them -- but the reality is everyone sort of cotton on to look, if you're building any product, whether it's for coding agents or for knowledge work or wealth management or whatever, you want to access multiple models, right? That's going to be the case. And that means there's lots of very careful design that you want to do, which is you want to have the harness not get coupled with the model layer. So people are getting pretty sophisticated in making sure that the harness layer is decoupled.

The other is the context layer also should be decoupled, right? You don't want effectively the one model to, in fact, vertically integrate into these 2. And that will be the game that will be played. But I'm pretty clear industry structure-wise, where we will go. Then the other question in the -- by the way, the other thing is people also will use a frontier model, like, let's say, there's something -- you have an eval, like everything is all about evals, right? So you have an eval, you want to max your eval, you use all these models.

Let's say, one particular model was doing well on that eval. The next thing you do is you take all your traces and then you train the model that's the cheapest to be better and hill climb on that eval. And so that's what everybody does, right? So it's no one wants to sort of be inefficient in their token spend, whether it's a company app or whether it is an ISV. And so the game would always be about continuous optimization of these models. And then the next thing that also -- you asked the question, how many models should be there, right? I think there should be as many models as companies because if you think about it, the real sovereignty question in the world, it's not about even country sovereignty.

It's about -- look, if really knowledge work, tacit knowledge of any enterprise of a firm is in the human capital they have, then it also needs to be in some model weights that they control. Otherwise, there's just going to be enterprise value transfer. Forget the software companies. It's like the rest of the economy. I mean that's where that [ Citrini ] thing sort of goes off on, which is that is what -- like -- and everybody is going to wake up.

I mean I just don't think we live in a real world and a real political economy. And so therefore, people are going to say, oh, no, that's not going to happen in my house. I'm going to have my model that is sovereign and independent of any frontier model. And that's the other sort of direction of travel.

Got it. Can we talk about the model that Microsoft is building. Of course, you have a great relationship with OpenAI. You have research IP, right? So you get access to their models. But Mustafa is also working on models within Microsoft. Is there a differentiation between what Mustafa is working on versus what OpenAI is working on?

Yes. So we have 2 sort of -- we have the OpenAI models and the OpenAI IP. And so we have -- essentially, if you look inside of Copilot, whether it's GitHub or M365, we have lots of models. It's not a single model. The one model would be a midtrained OpenAI lineage or an MAI model. And so we basically are continuing to optimize these models. And what are we optimizing for our evals, right? So all of us now at this point, the game is about private evals because we know the benchmarks are all saturated.

So the question is, how do you really make sure that any flops we assign to any research compute, right, whether it's post training of an OpenAI model or even our own model is all about maxing the evals that are private to -- so that means some product impact. That's the job #1. The second piece inside of that is all about COGS reduction, right, which is the auto mode, right? One of the things that I think this week or last week, there was a nice paper by the vLLM guys, the semantic router, right? If you remember when GPT5 came out, they had a router. And now even the sophistication of the routers are getting good, right? That means the models that you choose, the modes of the models you choose, the tools that you choose with it.

That's kind of -- that's -- think of it as a meta model, right? That's the most important thing. So our auto mode inside a Copilot, M365 Copilot, GitHub Copilot, is essentially doing that optimization of maxing the evals, reducing the COGS on a continuous basis. And so our frontier work, our super intelligence work will all be about really getting good and hill climbing on it. So the great news for us is we have access for whatever, 5 more years or what have you until '32. So therefore, we want to max the value of what the OpenAI R&D is while also making sure we have all the capability.

And it's no different than, by the way, what we are doing in silicon. I think you have Jensen here next, and we work deeply with him there, which he's doing lots of the innovation. And then we are also working on Maia and so a similar approach.

Got it. Got it. If we talk about the OpenAI relationship? It started 2018, very early before we were talking about any large language models or generative AI, but it's evolved a lot since then. What's the current status of that relationship? And how should we expect that to evolve on a go-forward basis? It's a very important relationship for Microsoft.

Yes. I mean I think -- yes, I mean, going back to 2018, I don't think any of us thought of -- we'll be here in 2026 talking about what is it, like close to $1 trillion company, right? I mean, that OpenAI is. And so we are thrilled to have invested in them, partnered with them. And although none of those things were sort of in my head at that time, who would have thought that I'll sort of earn the credit of being a good investor, but here I am. That's a great investment.

But the thing that was foundational to it, it's fascinating. I always think back at it. The paper I read, which, by the way, was authored, one of the authors was Dario, when he was at OpenAI on scaling laws. And that was the bet, which is when they all sort of sort of have the conviction, Ilya, Dario, Sam, all of these chaps were really into, hey, look, the scaling stuff works and we should go at it.

They showed me those charts, and I kind of understood it, but then I sort of said, now, these chaps are really good at this and they -- and I was always obsessed about NLP. So therefore, I wanted to take that bet. And so it's an important partnership. It's worked very well for them. It's worked very well for us. We also know that they're now a big company that they need some flexibility, and we've given them the flexibility. But they're also going to be a customer.

We're an investor. We are going to be a partner. And they're also -- even the latest partnerships they have are all fine by us because at some level, our exclusivities, our rev share, our equity partnership is all sort of what's going to be healthier because of all this.

For me, one of the things that has marked your tenure as CEO at Microsoft is really embracing openness and open ecosystems, right? I think one of the foundational moments for me is when you came on stage and said, we're going to treat Linux as a first-class citizen on Azure. It almost feels like OpenAI and OpenAI relationship is similar, right, that it behooves both parties to have more flexibility, more openness to let them grow, you get the IP, you get the revenue share, and you both benefit from a more open relationship.

Yes. I mean I think since this is an investor conference and not a feel good conference, right, the thing that I go back to is there are very few zero-sum battles. And I think we overstate that a lot. And so when the big strategic mistakes are made, when you're not clear about, wow, what could be an expansive way for me to think about my opportunity versus getting narrowly stuck on everything being a zero-sum battle.

In fact, our biggest mistakes, I would say, strategically would have been historically made when we didn't view -- like somebody else's success doesn't need to be a failure if you can write it. And it's sort of a thing that needs to be talked about more. And in fact, it is, like without Intel, I don't know if Windows would have happened, right? Without -- in fact, without Mac, I wonder whether Office would have happened, right? I mean that's sort of the world I come from, and I'm always looking for, first, what's the non-zero sum where we can add value to our customers. And then, of course, there are zero-sum battles and we'll compete.

Excellent. So if we talk about vertical integration. You talked about it a little bit in terms of needing to maintain flexibility with the harnesses and the context layers within the model. But there's also some really great innovation going on from Google, where they're integrating the TPU layer with the model layer. Is that something that Microsoft has to pursue more aggressively?

Because I know you guys do have your own silicon initiative. How important it is to have that strong integration between sort of the silicon layer and the evolving software layers on top of it?

It's -- I think the way I think about all of this is if you take the -- like the broad perhaps investor question on this, which is what's the best way to think about ROIC on capital spend? I would say there are a couple of different dimensions to it, right? One is you want TCO to be great. You can't be upside down on cost. Now my belief is, since this is a multi sort of generation thing, it's not about any one time, right? So you really want to make sure you are always going to have the best TCO.

And so that's why my fundamental bet is you want to have system software over a heterogeneous infrastructure. That way, then the latest innovation, right? I'm sure Jensen will come talk about sort of some very cool stuff he's working on next and let's call it, on some low latency inference. God, I want that, right? Why would I not -- like -- and maybe I'm also working on it, but it may be 2 years from now or 1 year.

But no, I want the best always in my fleet. And then I want my system software laid out on top of right? So that's kind of where I like our thing about, hey, we have a Maia 300, 200 today that does competitively, right? It's, in fact, marked on TCO per dollar -- performance per dollar, it's leading, in fact. But that's leading today that is not a guarantee that it's leading all the time. So therefore, I shouldn't get too high on my own supply here just because I have this means that's the end of the game.

No, I'm on that. And then let me then sort of make sure I'm also getting everybody else's innovation in, use software to then schedule across all of this and manage TCO by generation. Then the next layer, of course, is utilization, right? That's again a software game, really max the utility on it. Then make sure you have a diverse demand, right? We have 1P and 3P, even 3P. We have OpenAI book, we have Anthropic book, but we want to also have the long tail of enterprise IT. And so all 3 of these, I think, are more important than just like, oh, I have a model and sort of an accelerator, like TCO and cost economics is -- you got to have diverse customers, great utilization, multi-generation TCO curve. So it's a lot more sophisticated than just one simple thing.

Got it. You're talking about unit economics and the ROI of the CapEx spend. I think it's equally as surprised as we would have been in 2018 to be talking about how big OpenAI is. I think we'd be equally surprised to see how much we're spending on CapEx today, how much infrastructure we're building out in terms of data center. And it has caused concern amongst investors, does this fundamentally change the economics for Microsoft? Does this fundamentally make it a more capital-intensive business that's going to bring down gross margins? How do you see that equation playing out in the near term and then longer term?

Yes. I mean I think the -- I mean that capital intensity has been true even in the Claude era. And that, by the way, is the other thing that is probably worth pulling the thread on, right? We talk a lot about AI accelerators, which are obviously super important. But these agents are really driving up, guess what, regular compute uses, right? We like the innovation on the micro VMs and the containers and the security boundaries on this and storage requirements.

So basically, it's a full-on system upgrade of everything, the network, the compute, the storage, this AI accelerator. And that's all obviously all exploding. And so therefore, the capital intensity increases. So I fundamentally think that this is a capital-intense business, but it's a knowledge-intense business as well, right? So that's a software business. So that's sort of the way I think that this industry is probably unique in the sense that the -- you can't think of it as just a pure industrial. This is where interestingly enough, even on the schedule of how profit comes, right, you think about it, it's not like we have retired any GPUs yet.

Even though we have all the power constraints and so on, just because we have gotten our software so good and some of the workloads optimized, so good on that, that we are able to take the life of these things and really max them out. And so overall, I would say, yes, we have to manage a capital-intensive business, but using all the levers software gives us in managing TCO, managing utilization, optimizing the kernels by workload, ensuring that there's a diverse class of customers, using the diurnal curve even of utilization to create new SKUs to clear markets. I mean those are all things that I think will generate great ROIC.

And this is probably unique. I know the way we have set it up is you track the Azure KPI. The thing that I keep complaining to Amy always is like, hey, I love the Azure KPI, but I don't allocate my capital to Azure KPI, right? I allocate my capital to the Microsoft KPI because I have these -- all these other businesses that are using the same capital. So my 1P, and it has a different gross margin profile. So that's kind of at least how we will, between Amy and me, will take a look at the long-term ROIC and margin profile of the business.

And the margin dollars, not even just the margin percentage because even if it's a capital-intensive business, and we have the ability to use software to generate margin dollars, this will be a much bigger business than we ever had. I mean that was the thing. If you remember, Keith, it's funny that you've tracked us all through, right? I remember when I became CEO, everybody said, oh my God, isn't it too late man, like why bother to even build a public cloud because Amazon is so far ahead. And we knew it was going to be multiplayer. We knew that there is going to be margin, and we kept building. And so that is, I think, what -- and the reason why that was the case is it's software.

Got it. You touched on the allocations and capacity constraints, definitely a hot button topic for investors, the need to allocate towards R&D and first party versus just Azure. I was having dinner with [ Johnson Nielsen ] last night and said, the real solve to all of this is more capacity. It's kind of the first problem -- first-class problem to have of you have too much demand versus your capacity. How should we think about the time frame for that settling out for the capacity coming online that you could feed all of the mouths within Microsoft?

Yes. I think -- look, I think the challenge is what we are seeing is not just linear growth in some of the demand. And so therefore, I think the capacity challenge -- and it's also not any one thing, right? It's not just power, it's sort of silicon, it's wafers, it's substrates, it's memory, it's storage, it's everything. It's compute. And so I feel like it's going to take a lot more upstream capacity to get built, quite frankly, before we have some relief.

So this is where -- this is there to stay. Then the question is what you said, which is how do we make sure we allocate things here that are strategic. And that's where I think the tension is, right, which is -- and I get it, which is you would like -- and not all of you uniformly, but some of you would like that allocation to work in ways that just maximize the short term. That makes no strategic sense for Microsoft, right, or Microsoft investors who are long term because you want to be careful, right?

You don't want to sacrifice your own 1P, which is going to be higher margin by just not feeding that product. I mean that's not a sensible thing. Even like when we say, well, we're going to allocate to our R&D, as I said, we are not allocating to our R&D in ways to just sort of win some contest. No, I want to allocate my R&D to create a model that's going to COGS reduce, which is, again, sort of margins for investors long term, right?

So therefore, that is the asymmetry we will have, right, between what decisions I'll make versus what you may want me to make in the short term, given the supply constraint. And so that's where we will just have to be very disciplined and by the way, one other thing that I'll mention is we also want to be careful about pricing, which is -- look, we see this even in our supply side, which is people are going to take different strategies. Some are going to be very long-term oriented.

Some will be short-term oriented. And we'll remember that, by the way. And the thing that we want to be is long-term oriented even with our customers because these cycles will go -- come and go, and we don't want -- we want to earn trust in periods like this with customers who depend on us. And so these are all fairly important sort of criteria for us.

Outstanding. Unfortunately, that takes us to the end of our time, but really exciting times in software, really exciting times for Microsoft. feels like you guys are really positioned well for this opportunity. So thank you for coming.

And thank you, Keith, for your covering of us over all these years throughout my entire tenure, I deeply appreciate it. I know you're going off to do newer and better things. And so we wish you all the best on that one.

Thank you so much.

Greetings, and welcome to the Microsoft Fiscal Year 2026 Second Quarter Earnings Conference Call. [Operator Instructions] As a reminder, this conference is being recorded. It is now my pleasure to introduce Jonathan Neilson, Vice President of Investor Relations. Please go ahead.

Good afternoon, and thank you for joining us today. On the call with me are Satya Nadella, Chairman and Chief Executive Officer; Amy Hood, Chief Financial Officer; Alice Jolla, Chief Accounting Officer; and Keith Dolliver, Corporate Secretary and Deputy General Counsel.

On the Microsoft Investor Relations website, you can find our earnings press release and financial summary slide deck, which is intended to supplement our prepared remarks during today's call and provides the reconciliation of differences between GAAP and non-GAAP financial measures. More detailed outlook slides will be available on the Microsoft Investor Relations website where we provide outlook commentary on today's call.

On this call, we will discuss certain non-GAAP items. The non-GAAP financial measures provided should not be considered as a substitute for or superior to the measures of financial performance prepared in accordance with GAAP. They are included as additional clarifying items to aid investors in further understanding the company's second quarter performance in addition to the impact these items and events have on the financial results.

All growth comparisons we make on the call today relates to the corresponding period of last year, unless otherwise noted. We will also provide growth rates in constant currency when available as a framework for assessing how our underlying businesses performed, excluding the effect of foreign currency rate fluctuations. Where growth rates are the same in constant currency, we will refer to the growth rate only.

We will post our prepared remarks to our website immediately following the call until the complete transcript is available.

Today's call is being webcast live and recorded. If you ask a question, it will be included in our live transmission, in the transcript, and in any future use of the recording. You can replay the call and view the transcript on the Microsoft Investor Relations website.

During this call, we will be making forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could materially differ because of factors discussed in today's earnings press release, in the comments made during this conference call, and in the Risk Factors section of our Form 10-K, Forms 10-Q and other reports and filings with the Securities and Exchange Commission. We do not undertake any duty to update any forward-looking statement.

And with that, I'll turn the call over to Satya.

Thank you very much, Jonathan. This quarter, the Microsoft Cloud surpassed $50 billion in revenue for the first time, up 26% year-over-year, reflecting the strength of our platform and accelerating demand. We are in the beginning phases of AI diffusion and its broad GDP impact. Our TAM will grow substantially across every layer of the tech stack as this diffusion accelerates and spreads. In fact, even in this early innings, we have built an AI business that is larger than some of our biggest franchises that took decades to build. Today, I'll focus my remarks across the 3 layers of our stack, cloud and token factory, agent platform and high-value agentic experiences.

When it comes to our cloud and token factory, the key to long-term competitiveness is shaping our infrastructure to support new high-scale workloads. We are building this infrastructure out for the heterogeneous and distributed nature of these workloads, ensuring the right fit with the geographic and segment specific needs for all customers, including the long tail. The key metric we're optimizing for is tokens per watt per dollar, which comes down to increasing utilization and decreasing TCO using silicon systems and software. A good example of this is the 50% increase in throughput we were able to achieve in one of our highest volume workloads, OpenAI inferencing, powering our Copilots.

And another example was the unlocking of new capabilities and efficiencies for our Fairwater data centers. In this instance, we connected both Atlanta and Wisconsin site through an AI WAN to build a first of its kind AI super factory. Fairwater's 2-storey design and liquid cooling allow us to run higher GPU densities and thereby improve both performance and latencies for high-scale training. All up, we added nearly 1 gigawatt of total capacity this quarter alone.

At the silicon layer, we have NVIDIA and AMD and our own Maia chips, delivering the best all up fleet performance, cost and supply across multiple generations of hardware. Earlier this week, we brought online our Maia 200 accelerator. Maia 200 delivers 10-plus petaFLOPS at FP4 precision with over 30% improved TCO compared to the latest generation hardware in our fleet. We will be scaling this starting with inferencing and synthetic data gen for our Superintelligence Team as well as doing inferencing for Copilot and Foundry.

And given AI workloads are not just about AI accelerators, but also consume large amounts of compute, we are pleased with the progress we are making on the CPU side as well. Cobalt 200 is another big leap forward, delivering over 50% higher performance compared to our first custom build processor for cloud-native workloads. Sovereignty is increasingly top of mind for customers, and we are expanding our solutions and global footprint to match. We announced DC investments in 7 countries this quarter alone, supporting local data residency needs. And we offer the most comprehensive set of sovereignty solutions across public, private and national partner cloud, so customers can choose the right approach for each workload with the local control they require.

Next, I want to talk about the agent platform. Like in every platform shift, all software is being rewritten. A new app platform is being born. You can think of agents as the new apps and to build, deploy and manage agents, customers will need a model catalog, tuning services, harness for orchestration, services for context engineering, AI safety, management, observability and security. It starts with having broad model choice. Our customers expect to use multiple models as part of any workload that they can fine tune and optimize based on cost, latency and performance requirements. And we offer the broadest selection of models of any hyperscaler. This quarter, we added support for GPT-5.2 as well as Claude 4.5. Already over 1,500 customers have used both Anthropic and OpenAI models on Foundry. We are seeing increasing demand for region-specific models, including Mistral and Cohere as more customers look for sovereign AI choices, and we continue to invest in our first-party models, which are optimized to address the highest value customer scenarios such as productivity, coding and security.

As part of Foundry, we also give customers the ability to customize and fine-tune models. Increasingly, customers want to be able to capture the tacit knowledge they possess inside of model weights as their core IP. This is probably the most important sovereign consideration for firms as AI diffuses more broadly across our GDP and every firm needs to protect their enterprise value. For agents to be effective, they need to be grounded in enterprise data and knowledge, that means connecting their agents to systems of record and operational data, analytical data as well as semi-structured and unstructured productivity and communications data. And this is what we are doing with our unified IQ layer, spanning Fabric, Foundry and data powering Microsoft 365.

In the world of context engineering, Foundry knowledge and Fabric are gaining momentum. Foundry knowledge delivers better context with automated source routing an advanced agentic retrieval while respecting user permissions. And Fabric brings together end-to-end operational real-time and analytical data. 2 years since it became broadly available, Fabric's annual revenue run rate is now over $2 billion with over 31,000 customers, and it continues to be the fastest-growing analytics platform on the market with revenue up 60% year-over-year. All of the number of customers spending $1 million plus per quarter on Foundry grew nearly 80%, driven by strong growth in every industry. And over 250 customers are on track to process over 1 trillion tokens on Foundry this year. There are many great examples of customers using all of this capability on Foundry to build their own agentic systems. Alaska Airlines is creating natural language flight search. BMW is speeding up design cycles, Land O'Lakes is enabling precision farming for co-op members, and SymphonyAI is addressing bottlenecks in the CPG industry. And of course, Foundry remains a powerful on-ramp for the entire cloud. The vast majority of Foundry customers use additional Azure solutions like developer services, app services, databases as they scale.

Beyond Fabric and Foundry, we are also addressing agent building by knowledge workers with Copilot Studio and Agent Builder. Over 80% of the Fortune 500 have active agents built using these low-code/no-code tools. As agents proliferate, every customer will need new ways to deploy, manage and protect them. We believe this creates a major new category and significant growth opportunity for us. This quarter, we introduced Agent 365, which makes it easy for organizations to extend their existing governance, identity, security and management to agents. That means the same controls they already use across Microsoft 365 and Azure, now extend to agents they build and deploy on our cloud or any other cloud. And partners like Adobe, Databricks, Genspark, Glean, NVIDIA, SAP, ServiceNow and Workday are already integrating Agent 365. We are the first provider to offer this type of agent control plane across clouds.

Now let's turn to the high-value agentic experiences we are building. AI experiences are intent-driven and are beginning to work at task scope. We are entering an age of macro delegation and micro steering across domains. Intelligence using multiple models is built into multiple form factors. You see this in chat, in new agent inbox, apps, coworkers, scaffoldings, agent workflows embedded in applications and IDs that are used every day or even in our command line with file system access and skills. That's the approach we are taking with our first-party family of copilot spanning key domains.

In consumer, for example, Copilot experiences span chat, news, feed, search, creation, browsing, shopping and integrations into the operating system, and it's gaining momentum. Daily users of our Copilot app increased nearly 3x year-over-year. And with Copilot checkout, we have partnered with PayPal, Shopify and Stripe, so customers can make purchases directly within the app. With Microsoft 365 Copilot, we are focused on organization-wide productivity.

Work IQ takes the data underneath Microsoft 365 and creates the most valuable stateful agent for every organization. It delivers powerful reasoning capabilities over people, their roles, their artifacts, their communications and their history and memory all within an organization security boundary. Microsoft 365 Copilot's accuracy and latency powered by Work IQ is unmatched, delivering faster and more accurate work grounded results than competition, and we have seen our biggest quarter-over-quarter improvement in response quality to date. This has driven record usage intensity with average number of conversations per user doubling year-over-year. Microsoft 365 Copilot also is becoming true daily habit with daily active users increasing 10x year-over-year.

We're also seeing strong momentum with researcher agent, which supports both OpenAI and Claude, as well as agent mode in Excel, PowerPoint and Word. All up, it was a record quarter for Microsoft 365 Copilot seat adds, up over 160% year-over-year. We saw accelerating seat growth quarter-over-quarter and now have 15 million paid Microsoft 365 Copilot seats and multiples more enterprise chat users.

And we are seeing larger commercial deployments. The number of customers with over 35,000 seats tripled year-over-year. Fiserv, ING, NASA, University of Kentucky, University of Manchester, U.S. Department of Interior and Westpac, all purchased over 35,000 seats. Publicis alone purchased over 95,000 seats for nearly all its employees. We are also taking share in Dynamics 365 with built-in agents across the entire suite. A great example of this is how Visa is turning customer conversations data into knowledge articles with our customer knowledge management agent and dynamics. And how Sandvik is using our sales qualification agent to automate lead qualification across tens and thousands of potential customers.

In coding, we are seeing strong growth across all paid GitHub Copilot. Copilot Pro Plus subs for individual devs increased 77% quarter-over-quarter, and all up now, we have 4.7 million paid Copilot subscribers, up 75% year-over-year. Siemens, for example, is going all in on GitHub adopting the full platform to increase developer productivity after a successful Copilot rollout to 30,000-plus developers. GitHub Agent HQ is the organizing layer for all coding agents like Anthropic, OpenAI, Google, Cognition and xAI in the context of customers GitHub repos. With Copilot CLI and VS Code, we offer developers the full spectrum of form factors and models they need for AI-first coding workflows.

And when you add Work IQ as a skill or an MCP to our developer workflow, it's a game changer, surfacing more context like e-mails, meetings, docs, projects, messages and more. You can simply ask the agent to plan and execute changes to your code base based on an update to a spec-in Sharepoint or using the transcript of your last engineering and design meeting in Teams.

And we're going beyond that with GitHub Copilot STK. Developers can now embed the same run time behind Copilot CLI, multi-model, multistep planning tools, MCP integration, Ops streaming directly into their applications. In security, we added a dozen new and updated security Copilot agents across Defender, Entra, Intune, and Purview. For example, Icertis, the SOC team used Security Copilot agent to reduce manual triage time by 75%, which is a real game changer in an industry facing a severe talent shortage.

To make it easier for security teams to onboard, we are rolling out security copilot to all our E5 customers and our security solutions are also becoming essential to manage organization's AI deployments. 24 billion Copilot interactions were audited by Purview this quarter, up 9x year-over-year.

Finally, I want to talk about 2 additional high-impact agenetic experiences. First, in health care, Dragon Copilot is the leader in its category, helping over 100,000 medical providers automate their workflows. Mount Sinai Health is now moving to a system-wide Dragon Copilot deployment for providers after a successful trial with its primary care physicians. All up, we helped document 21 million patient encounters this quarter, up 3x year-over-year.

And second, when it comes to science and engineering, companies like Unilever in consumer goods and Synopsys in EDA are using Microsoft Discovery to orchestrate specialized agents for R&D end-to-end. They're able to reason over scientific literature and internal knowledge, formulate hypotheses, spin up simulations and continuously iterate to drive new discoveries.

Beyond AI, we continue to invest in all our core franchises and meet the needs of our customers and partners, and we are seeing strong progress. For example, when it comes to cloud migrations, our new SQL server has over 2x the IaaS adoption of the previous version. In Security, we now have 1.6 million security customers, including over 1 million who use 4 or more of our workloads. Windows reached a big milestone, 1 billion Windows 11 users up over 45% year-over-year. And we had share gains this quarter across Windows, Edge and Bing, double-digit member growth in LinkedIn with 30% growth in paid video ads.

And in gaming, we are committed to delivering great games across Xbox, PC, Cloud and every other device, and we saw record PC players in paid streaming hours on Xbox.

In closing, we feel very good about how we are delivering for customers today and building the full stack to capture the opportunity ahead.

With that, let me turn it over to Amy to walk through our financial results and outlook, and I look forward to rejoining for your questions.

Thank you, Satya, and good afternoon, everyone. With growing demand for our offerings and focused execution by our sales teams, we again exceeded expectations across revenue, operating income and earnings per share, while investing to fuel long-term growth. This quarter, revenue was $81.3 billion, up 17% and 15% in constant currency. Gross margin dollars increased 16% and 14% in constant currency, while operating income increased 21% and 19% in constant currency.

Earnings per share was $4.14, an increase of 24% and 21% in constant currency when adjusted for the impact from our investment in OpenAI. And FX increased reported results slightly less than expected, particularly in Intelligent Cloud revenue. Company gross margin percentage was 68%, down slightly year-over-year, primarily driven by continued investments in AI infrastructure and growing AI product usage that was partially offset by ongoing efficiency gains, particularly in Azure and M365 Commercial Cloud as well as sales mix shift to higher-margin businesses.

Operating expenses increased 5% and 4% in constant currency, driven by R&D investments in compute capacity and AI talent as well as impairment charges in our gaming business. Operating margins increased year-over-year to 47%, ahead of expectations. As a reminder, we still account for investment in OpenAI under the equity method. And as a result of OpenAI's recapitalization, we now record gains or losses based on our share of the change in their net assets on their balance sheet as opposed to our share of their operating profit or losses from their income statement. Therefore, we recorded a gain which drove other income and expense to $10 billion in our GAAP results.

When adjusted for the OpenAI impact, other income and expense was slightly negative and lower than expected driven by net losses on investments. Capital expenditures were $37.5 billion, and this quarter, roughly 2/3 of our CapEx was on short-lived assets, primarily GPUs and CPUs. Our customer demand continues to exceed our supply. Therefore, we must balance the need to have our incoming supply better meet growing Azure demand with expanding first-party AI usage across services like M365 Copilot and GitHub Copilot, increasing allocations to R&D teams to accelerate product innovation and continued replacement of end-of-life server and networking equipment.

The remaining spend was for long-lived assets that will support monetization for the next 15 years and beyond. This quarter, total finance leases were $6.7 billion, and were primarily for large data center sites. And cash paid for PP&E was $29.9 billion. Cash flow from operations was $35.8 billion, up 60%, driven by strong Cloud billings and collections. And free cash flow was $5.9 billion and decreased sequentially, reflecting the higher cash capital expenditures from a lower mix of finance leases. And finally, we returned $12.7 billion to shareholders through dividend and share repurchases, an increase of 32% year-over-year.

Now to our commercial results. Commercial bookings increased 230% and 228% in constant currency, driven by the previously announced large Azure commitment from OpenAI that reflects multiyear demand needs as well as the previously announced Anthropic commitment from November and healthy growth across our core annuity sales motions.

Commercial remaining performance obligation, which continues to be reported net of reserves increased to $625 billion, and was up 110% year-over-year with a weighted average duration of approximately 2.5 years. Roughly 25% will be recognized in revenue in the next 12 months, up 39% year-over-year. The remaining portion recognized beyond the next 12 months increased 156%. Approximately 45% of our commercial RPO balance is from OpenAI. The significant remaining balance grew 28% and reflects ongoing broad customer demand across the portfolio.

Microsoft Cloud revenue was $51.5 billion and grew 26% and 24% in constant currency. Microsoft Cloud gross margin percentage was slightly better than expected at 67%, and down year-over-year due to continued investments in AI that were partially offset by ongoing efficiency gains noted earlier.

Now to our segment results. Revenue from Productivity and Business Processes was $34.1 billion and grew 16% and 14% in constant currency. M365 Commercial cloud revenue increased 17% and 14% in constant currency with consistent execution in the core business and increasing contribution from strong copilot results. ARPU growth was again led by E5 and M365 Copilot, and paid M365 commercial seats grew 6% year-over-year to over 450 million with installed base expansion across all customer segments, though primarily in our small and medium business and frontline worker offerings.

M365 Commercial products revenue increased 13% and 10% in constant currency, ahead of expectations due to higher-than-expected Office 2024 transactional purchasing. M365 consumer cloud revenue increased 29% and 27% in constant currency, again driven by ARPU growth. M365 consumer subscriptions grew 6%. LinkedIn revenue increased 11% and 10% in constant currency driven by Marketing Solutions.

Dynamics 365 revenue increased 19% and 17% in constant currency with continued growth across all workloads. Segment gross margin dollars increased 17% and 15% in constant currency, and gross margin percentage increased, again driven by efficiency gains at M365 Commercial Cloud that were partially offset by continued investments in AI, including the impact of growing copilot usage.

Operating expenses increased 6% and 5% in constant currency, and operating income increased 22% and 19% in constant currency. Operating margins increased year-over-year to 60%, driven by improved operating leverage as well as the higher gross margins noted earlier.

Next, the Intelligent Cloud segment. Revenue was $32.9 billion and grew 29% and 28% in constant currency. In Azure and Other Cloud services, revenue grew 39% and 38% in constant currency, slightly ahead of expectations with ongoing efficiency gains across our fungible fleet, enabling us to reallocate some capacity to Azure that was monetized in the quarter.

As mentioned earlier, we continue to see strong demand across workloads, customer segments and geographic regions, and demand continues to exceed available supply. In our on-premises server business, revenue increased 2% and 1% in constant currency, ahead of expectations, driven by demand for our hybrid solutions, including a benefit from the launch of SQL Server 2025, as well as higher transactional purchasing ahead of memory price increases. Segment gross margin dollars increased 20% and 19% in constant currency. Gross margin percentage decreased year-over-year, driven by continued investments in AI and sales mix shift to Azure, partially offset by efficiency gains in Azure.

Operating expenses increased 3% and 2% in constant currency, and operating income grew 28% and 27% in constant currency. Operating margins were 42%, down slightly year-over-year as increased investments in AI were mostly offset by improved operating leverage.

Now to More Personal Computing. Revenue was $14.3 billion and declined 3%. Windows OEM and devices revenue increased 1%, and was relatively unchanged in constant currency. Windows OEM grew 5% with strong execution as well as a continued benefit from Windows 10 end of support. Results were ahead of expectations as inventory levels remained elevated with increased purchasing ahead of memory price increases.

Search and news advertising revenue ex TAC increased 10% and 9% in constant currency, slightly below expectations, driven by some execution challenges. As expected, the sequential growth rate moderated as the benefit from third-party partnerships normalized.

And in Gaming. Revenue decreased 9% and 10% in constant currency. Xbox content and services revenue decreased 5% and 6% in constant currency, and was below expectations driven by first-party content with impact across the platform. Segment gross margin dollars increased 2% and 1% in constant currency, and gross margin percentage increased year-over-year, driven by sales mix shift to higher-margin businesses.

Operating expenses increased 6% and 5% in constant currency, driven by the impairment charges in our gaming business noted earlier, as well as R&D investments in compute capacity and AI talent. Operating income decreased 3% and 4% in constant currency, and operating margins were relatively unchanged year-over-year at 27% as higher operating expenses were mostly offset by higher gross margins.

Now moving to our Q3 outlook, which unless specifically noted otherwise, is on a U.S. dollar basis. Based on current rates, we expect FX to increase total revenue growth by 3 points. Within the segments, we expect FX to increase revenue growth by 4 points in Productivity and Business Processes, and 2 points in Intelligent Cloud and More Personal Computing. We expect FX to increase COGS and operating expense growth by 2 points. As a reminder, this impact is due to the exchange rates a year ago.

Starting with the total company. We expect revenue of USD 80.65 billion to USD 81.75 billion or growth of 15% to 17%, with continued strong growth across our commercial businesses, partially offset by our consumer businesses. We expect COGS of USD 26.65 billion to USD 26.85 billion or growth of 22% to 23%, and operating expense of USD 17.8 billion to USD 17.9 billion or growth of 10% to 11%, driven by continued investment in R&D, AI compute capacity and talent against a low prior year comparable. Operating margins should be down slightly year-over-year.

Excluding any impact from our investments in OpenAI, other income and expense is expected to be roughly $700 million, driven by a fair market gain in our equity portfolio and interest income, partially offset by interest expense, which includes the interest payments related to data center finance leases. And we expect our adjusted Q3 effective tax rate to be approximately 19%.

Next, we expect capital expenditures to decrease on a sequential basis due to the normal variability from cloud infrastructure build-outs and the timing of delivery of finance leases. As we work to close the gap between demand and supply, we expect the mix of short-lived assets to remain similar to Q2.

Now our commercial business. In commercial bookings, we expect healthy growth in the core business on a growing expiry base when adjusted for the OpenAI contracts in the prior year. As a reminder, the significant OpenAI contracts signed in Q2 represents multiyear demand needs from them, which will result in some quarterly volatility in both bookings and RPO growth rates going forward. Microsoft Cloud gross margin percentage to be roughly 65%, down year-over-year, driven by continued investments in AI.

Now to segment guidance. In Productivity and Business Processes, we expect revenue of USD 34.25 billion to USD 34.55 billion or growth of 14% to 15%. In M365 Commercial Cloud, we expect revenue growth to be between 13% and 14% in constant currency with continued stability and year-over-year growth rates on a large and expanding base. Accelerating Copilot momentum and ongoing E5 adoption will again drive ARPU growth. M365 commercial products revenue should decline in the low single digits, down sequentially, assuming office 2024 transactional purchasing trends normalize.

As a reminder, M365 commercial products include components that can be variable due to in-period revenue recognition dynamics. M365 consumer cloud revenue growth should be in the mid- to high 20% range, driven by growth in ARPU as well as continued subscription volume. For LinkedIn, we expect revenue growth to be in the low double digits. And in Dynamics 365, we expect revenue growth to be in the high teens with continued growth across all workloads.

For Intelligent Cloud, we expect revenue of USD 34.1 billion to USD 34.4 billion or growth of 27% to 29%. In Azure, we expect Q3 revenue growth to be between 37% and 38% in constant currency against a prior year comparable that included significantly accelerating growth rates in both Q3 and Q4. As mentioned earlier, demand continues to exceed supply, and we will need to continue to balance the incoming supply we can allocate here against other priorities.

As a reminder, there can be quarterly variability in year-on-year growth rates depending on the timing of capacity delivery and when it comes online as well as from in-period revenue recognition depending on the mix of contracts. In our on-premises server business, we expect revenue to decline in the low single digits as growth rates normalize following the launch of SQL Server 2025, though increased memory pricing could create additional volatility in transactional purchasing.

In More Personal Computing, we expect revenue to be USD 12.3 billion to USD 12.8 billion. Windows OEM and devices revenue should decline in the low teens. Growth rates will be impacted as the benefit from Windows 10 end of support normalizes and as elevated inventory levels come down through the quarter. Therefore, Windows OEM revenue should decline roughly 10%.

The range of potential outcomes remains wider than normal, in part due to the potential impact on the PC market from increased memory pricing. Search and News advertising ex TAC revenue growth should be in the high single digits. Even as we work to improve execution, we expect continued share gains across Bing and Edge with growth driven by volume, and we expect sequential growth moderation as the contribution from third-party partnerships continues to normalize.

And in Xbox content and services, we expect revenue to decline in the mid-single digits against a prior year comparable that benefited from strong content performance, partially offset by growth in Xbox Game Pass. And hardware revenue should decline year-over-year.

Now some additional thoughts on the rest of the fiscal year and beyond. First, FX. Based on current rates, we expect FX to increase Q4 total revenue and COGS growth by less than 1 point with no impact to operating expense growth. Within the segments, we expect FX to increase revenue growth by roughly 1 point in Productivity and Business Processes and More Personal Computing, and less than 1 point in Intelligent Cloud.

With the strong work delivered in H1 to prioritize investment in key growth areas and the favorable impact from a higher mix of revenue in our Windows OEM and commercial on-prem businesses, we now expect FY '26 operating margins to be up slightly. We mentioned the potential impact on Windows OEM and on-premises server markets from increased memory pricing earlier. In addition, rising memory prices would impact capital expenditures, though the impact on Microsoft Cloud gross margins will build more gradually as these assets depreciate over 6 years.

In closing, we delivered strong top line growth in H1, and are investing across every layer of the stack to continue to deliver high-value solutions and tools to our customers. With that, let's go to Q&A, Jonathan.

Thanks, Amy. We'll now move over to Q&A. Out of respects for others on the call, we request that participants please only ask 1 question. Operator, can you please repeat your instructions?

[Operator Instructions] And our first question comes from the line of Keith Weiss with Morgan Stanley.

I'm looking at Microsoft print where earnings is growing 24% year-on-year, which is a spectacular result. Great execution on your part, top line growing well, margins expanding. But I'm looking at after-hours trading and the stock is still down. And I think one of the core issues that is weighing on investors is CapEx is growing faster than we expected and maybe Azure is growing a little bit slower than we expected. And I think that fundamentally comes down to a concern on the ROI on this CapEx spend over time. So I was hoping you guys could help us fill in some of the blanks a little bit in terms of how should we think about capacity expansion and what that can yield in terms of Azure growth going forward. More to the point, how should we think about the ROI on this investment as it comes to fruition?

Thanks, Keith. And let me start and Satya can add some broader comments, I'm sure. I think the first thing, I think you really asked a very direct correlation that I do think many investors are doing, which is between the CapEx spend and seeing an Azure revenue number. And we tried last quarter, and I think, again, this quarter to talk more specifically about all the places that the CapEx spend, especially the short-lived CapEx spend across CPU and GPU and where that will show up.

Sometimes, I think it's probably better to think about the Azure guidance that we give as an allocated capacity guide about what we can deliver in Azure revenue. Because as we spend the capital and put GPUs specifically, it applies to CPUs, the GPUs more specifically, we're really making long-term decisions. And the first thing we're doing is solving for the increased usage in sales and the accelerating pace of M365 Copilot as well as GitHub Copilot, our first-party apps. Then we make sure we're investing in the long-term nature of R&D and product innovation. And much of the acceleration that I think you've seen from us and products over the past a bit is coming because we are allocating GPUs and capacity to many of the talented AI people we've been hiring over the past years.

Then, when you end up, is that, you end up with the remainder going towards serving the Azure capacity that continues to grow in terms of demand. And a way to think about it, because I think, I get asked this question sometimes, is if I had taken the GPUs that just came online in Q1 and Q2 in terms of GPUs and allocated them all to Azure, the KPI would have been over 40. And I think the most important thing to realize is that this is about investing in all the layers of the stack that benefit customers. And I think that's hopefully helpful in terms of thinking about capital growth, it shows in every piece, it shows in revenue growth across the business and shows as OpEx growth as we invest in our people.

Yes, I think you -- Amy covered it. But basically, as an investor, I think when you think about our capital and you think about the GM profile of our portfolio, you should obviously think about Azure. But you should think about M365 Copilot and you should think about GitHub pilot, you should think about Dragon Copilot, Security Copilot. All of those have a GM profile and lifetime value. I mean if you think about it, acquiring an Azure customer is super important to us, but so is acquiring an M365 or a GitHub or a Dragon Copilot, which are all by the way incremental businesses and TAMs for us. And so we don't want to maximize just 1 business of ours, we want to be able to allocate capacity while we're sort of supply constrained in a way that allow us to essentially build the best LTV portfolio. That's on one side. And the other one that Amy mentioned is also R&D. I mean you got to think about compute is also R&D, and that's sort of the second element of it. And so we are using all of that, obviously, to optimize for the long term.

The next question comes from the line of Mark Moerdler with Bernstein Research.

Congrats on the quarter. One of the other questions we believe investors want to understand is how to think about your line of sight from hardware CapEx investment to revenue and margins. You capitalized servers over 6 years, but the average duration of your RPO is 2.5 years, up from 2 years last quarter. How do investors get comfortable that since this is a lot of this CapEx is AI-centric that you'll be able to capture sufficient revenue over the 6-year useful life of the hardware to deliver solid revenue and gross profit dollars growth, hopefully, one similar to the CPU revenue.

Thanks, Mark. Let me start with at a high level and Satya can add as well. I think when you think about average duration, I think what you're getting to is -- and we need to remember, is it, average duration is a combination of a broad set of contract arrangements that we have. A lot of them around things like M365 or our BizApps portfolio, are shorter dated, right, 3-year contracts. And so they have, quite frankly, a short duration. The majority then that's remaining are Azure contracts are longer duration. And you saw that this quarter when we saw the extension of that duration from around 2 years to 2.5 years. And the way to think about that is the majority of the capital that we're spending today, and a lot of the GPUs that we're buying are already contracted for most of their useful life. And so a way to think about that is much of that risk that I think you're pointing to isn't there, because they're already sold for the entirety of their useful life. And so part of it exists because you have this shorter-dated RPO because of some of the M365 stuff. If you look at the Azure only, RPO is a little bit more extended. A lot of that is CPU basis. It's not just GPU. And on the GPU contracts that we've talked about, including for some of our largest customers, those are sold for the entire useful life of the GPU. And so there's not the risk to which I think you may be referring. Hopefully, that's helpful.

Yes. And just to -- one other thing I would add to it is, in addition to sort of what Amy mentioned, which is it's already contracted for the useful life is we do use software to continuously around even the latest models on the fleet that is aging, if you will. So that's sort of what gives us that duration. And so at the end of the day, we want to have -- that's why we even think about aging the fleet constantly, right? So it's not about buying a whole lot of gear 1 year. It's about each year, you write the Moore's Law, you add, you use software, and then you optimize across all of it.

And Mark, maybe to state this in case it's not obvious, is that as you go through the useful life, actually, you get more and more and more efficient at delivery. So where you've sold the entirety of its life, the margins actually improved with time. And so I think that may be a good reminder to people as we see that, obviously, in the CPU fleet all the time.

The next question comes from the line of Brent Thill with Jefferies.

Amy, on 45% of the backlog being related to OpenAI. I'm just curious if you can comment, there's obviously concern about the durability. And I know maybe there's not much you can say on this, but I think everyone is concerned about the exposure. And if you could maybe talk through your perspective and what both you and Satya are seeing.

I think maybe I would have thought about the question quite differently, Brent. The first thing to focus on is the reason we talked about that number is because 55% or roughly $350 billion is related to the breadth of our portfolio, a breadth of customers across solutions, across Azure, across industries, across geographies. That is a significant RPO balance, larger than most peers, more diversified than most peers. And frankly, I think we have super high confidence in it.

And when you think about that portion alone growing 28%, it's really impressive work on the breadth as well as the adoption curve that we're seeing, which is I think what I get asked most frequently, it's grown by customer segment, by industry and by geo. And so it's very consistent. And so then if you're asking about how do I feel about OpenAI and the contract and the health, listen, it's a great partnership. We continue to be their provider of scale. We're excited to do that. We sit under one of the most successful businesses built, and we continue to feel quite good about that. It's allowed us to remain a leader in terms of what we're building and being on the cutting edge of app innovation.

The next question comes from the line of Karl Keirstead with UBS.

Amy, regardless of how you allocate the capacity between first party and third party, can you comment qualitatively on the amount of capacity that's coming on. I think the 1 gigawatt added in the December quarter was extraordinary and hence that the capacity adds are accelerating. But I think a lot of investors have their eyes on Fairwater Atlanta, Fairwater Wisconsin, and would love some comments about the magnitude of the capacity adds regardless of how they're allocated in the coming quarters.

Yes, Karl, I think we've said a couple of things. We're working as hard as we can to add capacity as quickly as we can. You've mentioned specific sites like Atlanta or Wisconsin, those are multiyear deliveries. So I wouldn't focus necessarily on specific locations. The real thing we've got to do, and we're working incredibly hard doing it, is adding capacity globally. A lot of that will be added in the United States, the 2 locations you've mentioned, but it also needs to be added across the globe to meet the customer demand that we're seeing and the increased usage. We'll continue to add both long-lived infrastructure. The way to think about that is we need to make sure we've got power and land and facilities available and we'll continue to put GPUs and CPUs in them when they're done as quickly as we can.

And then finally, we'll try to make sure we can get as efficient as we possibly can on the pace at which we do that and how we operate them so that they can have the highest possible utility. And so I think it's not really about 2 places, Karl, I would definitely abstract away from that. Those are multiyear delivery time lines. But really, we just need to get it done every location where we're currently in a build or starting to do that. We're working as quickly as we can.

The next question comes from the line of Mark Murphy with JPMorgan.

Satya, the performance achievements of the Maia 200 accelerator for inference, look quite remarkable, especially in comparison to TPUs and Trinium and Blackwell, which have just been around a lot longer. Could you put that accomplishment in perspective in terms of how much of a core competency you think silicon might become for Microsoft. And Amy, are there any ramifications worth mentioning there in terms of supporting your gross margin profile for inference costs going forward?

Yes, thanks for the question. So a couple of things. One is we've been at this in a variety of different forms for a long, long time in terms of building our own silicon. And so we're very, very thrilled about the progress with Maia 200, and -- especially when we think about running a GPT-5.2 and the performance we were able to get in the GEMS at FB4, just proof point that when you have a new workload, a new shape of a workload, you can start innovating end-to-end between the model and the silicon and the entire system. It's just not even about just the silicon, the way the networking works at rack scale that's optimized with memory for this particular workload.

And the other thing is we're obviously round-tripping and working very closely with own super intelligence team with all of our models, as you can imagine, whatever we build will be all optimized for Maia. So we feel great about it. And I think the way to think about all up is we're in such early innings. I mean, even just look at the amount of silicon innovation and systems innovation. Even since December, I think the new thing is everybody is talking about low latency inference, right? And so one of the things we want to make sure is we are not locked into any one thing. If anything, we have great partnership with NVIDIA, with AMD, they are innovating, we're innovating. We want a fleet at any given point in time to have access to the best TCO. And it's not a one-generation game. I think a lot of folks just talk about who's ahead. It's just remember, you have to be ahead for all time to come. And that means you really want to think about having a lot of innovation that happens out there to be in your fleet, so that your fleet is fundamentally advantaged at the TCO level. So that's kind of how I look at it, which is we are excited about Maia. We're excited about Cobalt. We're excited about our DPU, our NIC. So we have a lot of systems capability. That means we can vertically integrate. And because we can vertically integrate doesn't mean we just only vertically integrate. And so we want to be able to have the flexibility here, and that's what you see us do.

The next question comes from the line of Brad Zelnick with Deutsche Bank.

Satya, we heard a lot about Frontier transformations from Judson at Ignite, and we've seen customers realize breakthrough benefits when they adopt the Microsoft AI stack. Can you help frame for us the momentum in enterprises embarking on these journeys? And any expectation for how much their spend with Microsoft can expand in becoming frontier firms?

Yes. Thank you for that. So I think one of the things that we are seeing is the adoption across the 3 major suites of ours, right? So if you take M365, you take what's happening with security and you take GitHub. In fact, it's fascinating. I mean these 3 things had effectively compounding effects for our customers in the past, like something like Entra as an identity system, or Defender as the protection system, across all 3 was sort of super helpful.

But so what now you're seeing is something like Work IQ, right? So I mean just to give you a favor for it, the most important database underneath for any company that uses Microsoft today is the data underneath Microsoft 365. And the reason is because it has all those tacit information, right, who are your people, what are their relationships, what are their projects they're working on, what are their artifacts, their communications. So that's a super important asset for any business process, business workflow context.

In fact, the scenario I even had in my transcript around you can now take Work IQ as an MCP server and GitHub Repo and say, "Hey, please look at my design meetings for the last month in Teams and tell me if my repo reflects it." I mean that's a pretty high-level way to think about how, what is happening previously perhaps with our tools business and our GitHub business are suddenly now being transformative, right? That agent black plane is really transforming companies in some sense, right? That's, I think, the most magical thing, which is you deploy these things. And suddenly, the agents are helping you coordinate, bring more leverage to your enterprise.

Then on top of it, of course, there is the transformation, which is what businesses are doing. How should we think about customer service. How should we think about marketing. How should we think about finance. How should we think about that and build our own agents. That's where all the services in Fabric and Foundry. And of course, GitHub tooling is helping them or even the low-code/no-code tools. I had some stats on how much that's being used. But one of the more exciting things for me is the new agents systems, M365 Copilot, GitHub Copilot, Security Copilot, all coming together to compound the benefits of all the data and all the deployment, I think, is probably the most transformative effect right now.

Thanks, Brad. Operator, we have time for 1 last question.

And the last question will come from the line of Raimo Lenschow with Barclays.

The last few quarters we talked -- besides the GPU side, you talked about CPU as well on the Azure side and you had some operational changes at the beginning of January last year. Can you speak what you saw there? And maybe put it more on a bigger picture in terms of clients realizing that their move to the cloud is important if you want to deliver proper AI. So what are we seeing in terms of cloud transition?

I didn't quite...

Sorry, Raimo, you were asking about the SMC CPU side? Or can you just repeat the question, please?

Yes. Sorry. So I was wondering about the CPU side of Azure because we had some operational changes there. And we also hear from the [ field a lot ] that people are realizing they need to be in the Cloud if you want to do proper AI and if that's kind of driving momentum.

Yes. I think I get it. So first of all, I had mentioned in my remarks that when you think about AI workloads, you should think of AI workloads as just AI accelerator compute, right? Because in some sense, you take any agent, the agent will then spawn through tools used maybe a container, which runs obviously on compute. In fact, we have -- whenever we think about even building out of the fleet, we think of in ratios or even for a training job, by the way. An AI training job requires a bunch of compute and a bunch of storage very close to compute. So therefore -- and same thing in inferencing as well.

So in inferencing with agent mode would require you to essentially provision a computer or computing resources to the agent. So not -- they don't need GPUs. They're running on GPUs, but they need computers, which are compute and storage. So that's what's happening even in the new world.

The other thing you mentioned is the Cloud migrations are still going on. In fact, 1 of the stats I had was SQL -- latest SQL server growing as an IaaS service in Azure. And so -- that's one of the reasons why we have to think about our commercial cloud and keep it balanced with the rest of our AI Cloud because when clients bring their workloads and build new workloads, they need all of these infrastructure elements in the region in which they are deploying.

That wraps up the Q&A portion of today's earnings call. Thank you for joining us today, and we look forward to speaking with you all soon.

Thank you all.

Thank you.

Thank you. This concludes today's conference. You may disconnect your lines at this time, and we thank you for your participation. Have a great night.

Good morning.

Good morning, everyone.

Good morning for day 2. Actually, I'll leave out the day with like 2 congratulations. Judson and Craig, both of you have expanded roles in the organization. So first of all, congratulations.

Thank you.

Thank you.

The Judson, so at the moment, it's really exciting times if you think about the technology world. Can you -- one of the things that was really interesting when I was here at Incyte a couple of weeks ago was this notion of the frontier transformation. Can you talk a little bit -- what does it mean to you? What's the message here?

Yes. So we're really working with customers to have sort of an evolution in the wave of AI transformation because if you look at the first couple of years of progress, largely focused on efficiency and productivity and largely tech-driven. And if you look at the corpus of AI projects out there in the market, I think Craig would agree with this, you see that there is an extraordinarily high failure rate of AI projects, north of 80%, depending on the research that you study.

And if you pick apart the reasons why, some of it is classic tech to business, misalignment with business goals. Others are tied back to sort of the disorganization of data in the enterprise and lacking a real data estate or foundation to build quality and capabilities.

And then if you poke further, it's down into not having the right kind of AI development tools that allow researchers to become productive in an environment. And so if we look at by contrast, the places where we have seen strong success across our customer base, across multiple industries and around the world, it's largely tied to this notion of business-led transformation. What are you doing to enrich your employee experience and tie that back to your own KPIs? How are you're looking at it in the frame of customer engagement, driving top line revenue, not just bottom line savings?

How are you reshaping business processes, not just throwing tech at existing ones, but actually stopping and saying, how do I create an AI-first business process that's grounded in human ambition and empowered by assistance in an agent ecosystem?

And then four, how are you putting AI to work for innovation? If you have this success framework where you're tying it back to business-led transformation and then applying the technology portfolio to it, the success rate goes up materially. So we're coining this notion of frontier transformation as being a business-led AI evolution that really allows for a fundamental reinvention of the business empowered by AI rather than the other way around. So it may sound like a subtlety, but there is a massive difference in how we're tracking progress with customers on these business-led transformations. And so much of what we've announced in terms of product portfolio is meant to help and stimulate that kind of growth.

Is that -- and how do you compare and contrast that to like previous tech transformation, we had like the move to the cloud. We had the Internet, et cetera, like this feels slightly different.

I mean I think it's fair to say almost every wave of technology has been largely grounded in efficiency, right? How do I throw tech out of process, make it faster, drive down cost? And AI certainly has the potential to do that, right?

I mean much of what you read about is the impact on white-collar work and how is it going to change? But if you pull the thread on it, the bottom line is AI can do a lot more for humanity and needs to do a lot more for humanity than simply drive efficiencies. And so if you look at AI and drug discovery, for example, we are shaving an order of magnitude off the cycles that are required to get new drugs to market and new drugs to market safely without having to risk human trials, only to have them fail in the last mile. And there's objective evidence of making real progress there. The same can be said with material science, advanced -- it's in quantum and even in our own Quantum labs at Microsoft, I think, is a big reason why we're ahead in getting quantum chipsets to market is because of the use of AI and material science.

And so if you pivot away from, gosh, how can I just save money and eliminate jobs with AI versus like, hey, actually, how can I get more done? How can I unlock creativity and innovation with AI? It's a remarkable change in terms of the business outcomes that you can drive.

And then that's actually a good segue into my first question for Craig, like our industry, like financial services, we -- sorry, if I say that, like historically, you're not like first adopters, but like I see a big movement around AI. How do you feel about AI for us?

The trends. So look, I think we'd all agree the opportunity is quite significant. And virtually everything about AI is changing at pace. When I sort of reflect on some of the key trends and probably reflecting on the more recent influence of sort of generative AI, sort of 5 things sort of come to mind.

Firstly, I think you're starting to see the shift from experimentation into operationalization, slower than, in fact, what we might have anticipated a little bit to Judson's point. But AI is starting to change roles and workflows in a really fundamental way. Take your customer service agent as an example, who now has real-time expert support, guiding them through a call, summarizing the results of that call, assessing their development needs throughout that call and then in a position to create personalized development content, that's not augmentation. It's a shift in the way work is happening and it's a shift in how capability is being developed.

And you're starting to see that pattern across operations, across technology, across trust risk management. I think the second thing that you see happening, Raimo, is this shift from AI use in back office to the point of customer interface from human in the loop to sort of more autonomous workflows.

And the significance of that shift is it elevates the importance of governance. And by that, I don't mean just guardrails. I mean understanding the role of each agent in the organization, be it human or AI, understanding their commissions, understanding the decisions they make, where you need to have oversight. And I think the organizations that get that right will establish autonomy with accountability.

And within financial services, that's going to be key. I think the third thing that I see, and Judson touched on this, is whilst we often talk about AI, the real battleground is, in fact, data. Yes, of course, data fuels AI. But too often that data is not accessible, not of the right quality, not structured in the way to support real-time decisions.

And I think most organizations are discovering the same fundamental truth. And that is you cannot scale AI while your data is trapped in silos, while you can't link activity across the full customer life cycle and while you don't have the lineage and controls in your data to have trust in that data.

So while AI often gets the headlines, actually, it's in data that I think real advantage will be created. Then I think you're seeing infrastructure being recreated. I think we all will sort of understand that to use AI at scale, yes, I need access to powerful models across multiple clouds. Yes, I need increased compute density. Yes, I need much greater power capacity.

But I think the really important shift is in resilience that is AI, in particular, agents and autonomous agents become more integral to critical workflows in organizations like financial institutions, the expectations on resilience will actually raise quite sharply. And I think that's going to have a profound impact across the entire stack from networks to multi-cloud infrastructure to data platforms to APIs.

And I think you're going to see much greater focus come on to this question of resilient AI provisioning within organizations. And then finally, I think AI is emerging as much as a threat as it is an opportunity. You think about those traditional cyber exploits, identifying vulnerabilities, polling of systems, creating malware, all of that can be executed now at a scale and pace that just wasn't attainable before.

But it's probably the new exploits that are emerging or the new opportunities for exploits that are emerging that are most concerning. Think of a mass disinformation campaign executed across multiple social platforms, a vast target audience using deep fakes executed almost instantly. That's a very different type of threat that we now need to begin to mitigate and arguably probably one of the more important of those sort of -- those trends.

Yes. And then Judson, Craig mentioned a couple of interesting points, data as a really core kind of point here as well. How do you think about it at Microsoft in itself? Like yesterday, I was talking with Gina from ServiceNow and asking her about is she eating her own dog food. She's had champagne actually. like what's the -- how are you going about it? And how do you think about that data aspect as well?

Yes. So the data piece of it is super critical for us. I mean we just had our Ignite conference a couple of weeks ago and had over 70 product announcements, but really in sort of 2 major areas, one around intelligence and the other on trust, both sort of really grounded in data.

And what we did on the intelligence front was a pretty massive lift to help our customers get their arms around their data in a much more usable way. Let me explain this in a little bit of detail here just so you all can understand because there's a fundamental difference on asking AI engineers, even the best and brightest of AI developers to go after data sources using connectors and APIs versus using a semantic layer through what we call MCP servers, allowing agents to speak to agents. If you take a model provider's approach towards trying to synthesize a business process, a model provider will take data through 1,000 small straws, hoover it up and try to inference over that data, and we use inference as if it's a super cool smart technical word, it's fancy guessing. So you're hovering up tons of data through lots of small straws and guessing on an outcome.

What we've done by contrast is to serve up these intelligent layers. The first one being what we call Work IQ. Work IQ is basically the brain inside of Microsoft 365 Copilot. It knows how you work, with whom you work, the content over which you collaborate, and it knows it precisely, not guessing. And those are actual workflows. When you delegate an important task, it knows to whom you delegate it and has the history of how you've done so for years.

We've served up Work IQ now, not just as the brain inside of Microsoft 365 Copilot, but basically as an Azure meter. So you can build agents on top of this intelligence layer that provide a higher degree of accuracy, speed and trust. You can reason over confidential and encrypted documents, if that's what your rights and privileges entitle you to do. And then it's a material leap forward versus what a model provider can do or any other company that is trying to somehow sit data through 1,000 straws or frankly, an enterprise customer trying to have their AI engineers do that themselves. It's super hard. We've done this at every layer of the stack. We've also done it on Fabric.

We released Fabric IQ. Fabric is our data services product that allows you to reason over multiple different data sources. So BigQuery running on Google Cloud platform or Amazon S3 data stores and of course, all of the Azure data services and even data services running in your environment. With Fabric IQ, what we've done is we've taken the semantic layer inside of Power BI. So the sort of the one binding link across all of those data services across multiple clouds is the fact that most of them use Power BI to understand the semantic layers within their business.

We've also served that up now as a singular API, so you can reason over data far more accurately in the way in which your business understands it through Fabric IQ regardless of what cloud you want to run on. And then, of course, then the final tier, the foundry layer where AI applications are actually built. We have unlocked all of the knowledge bases that sit into agent-to-agent communication, all of the Azure search foundational elements into Foundry IQ. So you can develop agents far more effectively and efficiently than ever before with this intelligent layer. And everything I've just described to you is model diverse.

So a new model comes out tomorrow, great, snap it in. We support over 11,000 models. It's open and heterogeneous at every layer of the stack. And so we think -- back to this like how do we have to drive frontier transformation versus throwing tech at existing business processes and hoping things get better. You have to serve up this layer of intelligence to allow business to get more work done. And so it's been a big investment for us, and I think a pretty strategic advantage for customers that rely on and trust Microsoft.

Yes. Perfect. And then, Craig, like if you think about it on the Barclays side, like there's so many potential things that you -- that AI could touch. Like how do you think about our journey? Like what's the -- how do you prioritize about that?

Yes. So look, I think we were quite early into generative AI, largely off the back of, I think, the investments we've made across infrastructure, data security, those foundations. And if I sort of wind forward to where we are today, it's emerging out of what you might want to think of as the creative chaos phase, lots of experimentation, rapid learning, uneven value. But I think through that, a level of insight around where the opportunities are and how we need to go about executing against those opportunities, sort of 3 areas that I think are priorities.

The first, and Judson touched on, this is process transformation. And I tend to think of generative AI, in particular, as almost the third generation of automation, the first rule-based, the second, third base, now language-based. And it's when you combine the potential of AI, digital and data that I think you create an enormous opportunity to rethink, redesign process to unlock true -- real value there.

And I think this is Judson's point around frontier transformation. It's about working front to back and left to right across the organization, and that's exactly what we're doing. Two, colleague enablement, just getting the technology into the hands of colleagues, allowing them to innovate, simple augmentation, more complex autonomous workflows.

For me, the challenge there isn't a technical challenge. I'm not even sure it's a skills challenge. Yes, skills is important. It's absolutely a mindset challenge. It's about how you actually inspire people to sort of embrace the art of the possible, set aside traditional ways of working, invest the time and energy in discovering what you can do with this technology. And I would say that's a war not yet won, but certainly one that we're really focused on. And then finally, tech modernization. And that goes beyond just software development. It's about how we're using AI to shift from legacy architectures, legacy technologies, legacy practices to more contemporary ones. It's an area where we see sort of crews, so cross-functional teams of agents and engineers having enormous potential. It's allowing us to refactor legacy technology in a fraction of the time that we previously did. So the opportunity to accelerate tech modernization far greater than what we might have seen even 2 years ago.

Now I would say this, Raimo, if you'll ask me this question again in 12 or 24 months' time, I may very well have a different answer, right? And I think it's the nature of this technology. We're still learning. The technology is evolving really quickly. So what's key is, yes, be curious, be adaptable, keep really focused on customer value is the only way I think you can approach this.

And Judson, from your customer conversations, like I'm sure you're getting that as well, like how do I think about the return like -- of all these kind of adopting this innovation, et cetera? Like what's the conversation like?

So that was the second big part of our work at Ignite. I mentioned intelligence and trust. On the trust side -- trust means a lot of things, of course, there's a security element to trust, which I'll come to. But there's just good old-fashioned trust in the business and trust in the partnership is this journey that we're about to embark upon of embracing AI to reinvent business processes and become frontier is that something that we can trust.

Can we trust that the ROI at the end is actually going to be something that makes the juice worth to squeeze, right? So we announced a lot of new capability around this observability aspect that Craig mentioned, we announced a new product called Agent 365. What Agent 365 allows you to do is basically visualize all of your AI artifacts across the entire enterprise, whether they're built on the Microsoft platform or any other third-party platform.

And allows you to register these agents, provide identity and access privileges to them. But far more importantly, it actually allows you to visualize how they come together in workflows and how they come together with human interaction across those workflows. So we turned on the product because part of the new role is I've inherited IT at Microsoft. We turned on Agent 365 before we launched and announced the product. And we have 138,000 agents being used by 88,000 employees on a weekly basis, which I would offer you all up to turn on Agent 365, it's free in your environment because I would be willing to bet you have more AI happening inside your organization than you know about.

But the beauty of this is you can instantly understand how these agents come together in a process, in a workflow and then also the usage intensity so that you can then go back and optimize. And say within a supply chain flow, for example, you can say, well, we have an out-of-stock agent and an inventory agent and availability to promise agent, wow, it looks like the availability to promise agent is getting hit by a lot of people.

The usage intensity is really high, and it's costing us a lot of money and actually impacting ROI on the overall process. So you can actually zero in on that, go in and understand what the model is underneath that agent, fine-tune the model, optimize the process and streamline it. And you can actually do this as a part of the dev process even before you decide to say, I'm going to take these agents -- production in the wild.

So we could meet with a large insurance company who wants to reinvent claims processing with AI. And before they have to commit to some massive body of work, we can actually run water through, here's what an AI claim cost to process per claim.

And if you're going to do 100 million claims in a year, great. This is the bill. Here's the ROI. You actually see these things upfront, manage the security aspects of the same. It all binds to the same identity platform that you use for your employees and your contingent staff, you can register your agents that way. And you can use all of our data classification tools to make sure that the data over which those agents are reasoning is what you, in fact want. And so this idea of providing observability at every layer of the stack with transparency around the ROI for AI is also something we think is going to be a huge lift for the acceleration of real AI adoption because if you can build with intelligence and then trust the process and understand the ROI before you invest, we think it's a pretty big unlock.

Yes. And talk about trust, like Craig, when we made the decision to roll out Copilot, to me, that was probably the single most kind of project I've seen in Barclays since I've been here, and that's kind of many years...

Aided by my friends on the left here.

Yes, yes. Can you a little bit -- can you talk to that like it seems like there was an urgency that I haven't seen before. What was the thinking here? And what are you seeing in terms of outcomes so far?

Sure. So look, I think when we started with Copilot, we started, as you do with often with new technologies, which is sort of a small pilot to understand impact. I think what was really surprising, Raimo, was the groundswell of interest in being involved more so than I think any other technology that I've had the opportunity to bring into an organization.

What we learned from those early pilots is that when we put the technology in the hands of colleagues, they did innovate. They did find new ways of working for themselves and their teams. And that gave us confidence to scale and make the commitment to enabling 100,000 colleagues across the organization. Now with that commitment then came also a challenge, which is how do you create across the broader organization what we actually saw in those early adopters and innovators?

How do you inspire the same enthusiasm, the same curiosity, the same desire to discover the potential and capability of this technology on a much, much larger scale? So we lent into that really hard. We ran 3 global hackathons, 6,000 colleagues involved in those, another 8,000 that we didn't have capacity for. And I say that just as a measure of the scale of interest across the organization.

That wasn't just tech, that was organization-wide. We ran escape rooms, prompt the fonds, hundreds of demos and fostered quite a sort of a vibrant community of interest across, firstly, teams and then Viva Engage. So a lot of energy in creating sort of not just skill and understanding, but also the mindset shift and holding up role models within the organization.

Where are we today according to Viva Insights, we've just crossed 1 million hours of productivity through that process. But actually, the other measure, I think, is just the demand that continues to exist. And that demand has meant that the enablement that was to run to the middle of next year will now be concluded at the end of this year. Where to next? Look, it is about continuing to inspire, continuing to educate, hold up the role models, putting new capability, Copilot agents into the hands of those early adopters and innovators and seeing what they can do with autonomous workflows in support of themselves and their teams.

Judson, is it -- are we like a typical customer there? And then the other thing I had to -- that one is like it does feel like the Copilot initially was seen as like, okay, I ask a question and do something more, but it -- now like it feels much, much broader. And I'm only now realizing, okay, this is actually the gateway to everything.

No. Look, we've had a very strong relationship for a long time. And Craig and I have done a lot of work together across our teams. The partnership has been strong because of the feedback loop between the organizations.

I think Craig has been very strong at helping us even taste make the product itself. So Barclays on that side of things is a strong early adopter. I think the other thing just to even kind of come back to the Frontier concept, it's no accident that Craig is now in the role that he's in, right?

Because you move from just leading technology to leading the business and applying AI for the outcomes is frankly where we're all headed. So if you take that back to the comparison of the Copilot journey itself, you were to make an abstraction, Copilot is for AI like the iPhone is for personal computing or like Windows was for the PC. It's a platform.

It's designed to drive a lot of great personal productivity, and it is achieving that. It's the fastest-growing product we've ever launched with the highest utilization. Agents are basically like the apps on your iPhone. They provide that accelerant into the process that you're trying to achieve, the ambition you're trying to unlock, the creativity or the innovation that you're trying to go and pursue.

And so this next step that Craig talks about connecting agents back into the flow of how humans get things done, how they innovate. That is where we are right now, sort of the apex of where we are with the bulk of our customers. We're super excited about the adoption of Copilot. We think we're just getting started in terms of the real AI unlock because if you couple human ambition with Copilot and an agent ecosystem, that's really the formula for really driving true frontier transformation across every facet of the business across all industries.

Yes. And then last question for me, and then I have to let you go. Time fly -- flew by here quickly. if you look out the AI potential that you see out there, how do you think about kind of adoption curves, but also kind of the next steps that are coming out of that? And it's more like we saw a Copilot, but like what's the stuff that we are not thinking about at the moment?

I think it actually comes down to this reinvention of the business that has to be pursued, right? And so there is so much we can achieve with the technology that's less of the question and more of the applied use of the same. I'm very excited about where we're headed with these Agentic business processes with empowering our customers with this intelligence layer that allows people to build those more effectively, more efficiently and with greater confidence.

And then at the same time, the counterbalance on observability so that there are some predictions on the ROI and the outcomes, I think that's a huge confidence booster for business and the adoption cycles. And I do think you'll see a lot more definitive cases of real growth and abundance versus simply efficiency and productivity.

Yes. Craig, for you, same question?

Yes. So look, I think it's redefining what digital transformation means for organizations like ours and increasing its urgency. It is a uniquely powerful capability that changes everything. It changes how we think about designing personalized intelligent digital experiences. It changes how we think about creating autonomous workflows. It changes how we think about modernizing our technology.

And I think it's both distinctive but also new. So there's a sort of a window of opportunity at the moment to differentiate. And I don't think anyone can afford to be left behind, which is why you see the greater urgency. But I think it requires some really important shifts. Judson called out the one that I think is absolutely right, which is this sort of frontier transformation. It's, hey, look, pick 3 or 4 key journeys, 3 or 4 end-to-end processes, go deep, not experimentation, real transformation. And in that, solve not just with AI, but the integrated capability of digital data and AI because the 3 in combination are far greater in capability.

Then I think this issue of just preparing the organization for adoption. The technology is ready. The question is, is the organization ready? And I think that's a different question. It comes down to how you get the technology in the hands of people, how you create the right climate, the right mindset, how you start to educate leaders to manage teams that are now hybrid teams of agents and individuals.

And then this sort of question of just getting the foundations right, like -- getting infrastructure right, getting data right, getting security right, none of that stuff is glamorous. But ultimately, it will set apart those that actually pilot AI and those that really scale the capability, and I think they're critical.

Yes. But it sounds like an exciting journey. And I'm glad I'm on as well. Thank you.

Thanks.

Thank you.

Okay. Let's get started. I'm Karl Keirstead covering Microsoft here, and we are always so honored to have Microsoft keynote our event. I was saying to Rajesh, it wouldn't be the same without Microsoft up on stage. So Rajesh, thank you so much for flying in for this.

Well, thank you so much for having me, Karl.

Yes. Rajesh, when I went online to look at all the parts of Microsoft that report up to you, it took me a little while because there are so many, but I summed up the revenue stream, and it was a very, very large number. So a very big part of Microsoft's portfolio sums up to this gentleman. Can you talk a little bit about what some of the common threads are between the different parts of the portfolio that report into you?

Yes. So I lead the experiences and devices at Microsoft. It's got Office, Teams, so M365. It has our business applications, Dynamics, Power platform, Windows, Surface and then, of course, M365 Copilot. And the common theme, Karl, for experiences and devices, it's what the name suggests, the experiences and devices for information workers on the globe to allow them to be productive, to be collaborative and ultimately drive business outcomes, economic opportunity. So it's a per user. Think of it as we are focused on the users in these organizations, small or large.

Got it. That makes sense. So I think the -- there are several areas we're going to talk about, but clearly, one of the largest pieces is the M365, Office 365 portfolio, where it's still amazing to me that, that business as large as it is and as ubiquitous a product it is, is still growing by 15%. And Amy gave some good stats around seat growth and ARPU growth. But maybe you could just describe a little bit about the underlying growth drivers to get to that 15%. What's really behind the numbers, Rajesh, and in a way that might help us get confidence in the durability of, let's say, a teens growth rate?

Well, M365, as we've said, is we are upwards of 400 million subscribers, paid subscribers. And the seat growth, we continue to see a healthy seat growth, even though we are mature in the enterprise space, small businesses, medium-sized businesses, first-line workers continue to drive seat growth for us. But the biggest drivers of the business now are really on the ARPU growth side. And the ARPU growth are primarily driven out of customers, 3 things that drive the ARPU growth. The first is customers choosing all of our suite, which is they may have in different flavors, different offers and they go to M365, the entire suite. And then the premium offering of M365, which is our E5 offering is probably the largest driver of the ARPU growth. And then increasingly, and in fact, the second largest driver of the ARPU growth is Copilot. So we feel good about continued seat growth. We feel good about the ARPU growth. And yes, it's a large PxQ business.

Let's -- we'll talk about a few of those things, but maybe let's start with the seat side. So I think in the most recent quarter, you guys announced 6% seat growth. Again, still amazing given the scale of that business. Obviously, investors for all SaaS businesses are worried about headcount cuts. Now the pace at which that seat growth is declining to me, speaks more to just scale and maturity. I don't really see any strong evidence that there's sort of an AI-induced seat pressure in that number yet. Do you mind opining a little bit on that dynamic?

No, we don't see that either. I'm not seeing AI as driving down seats in M365. If anything, I think AI is going to be an opportunity for us to drive seat growth. If you think about an organization in the future, it's going to have more agents than people. Many of these agents are going to be embodied. They're going to have an identity. They're going to be in the address book. They're going to have a mailbox. They're going to need a computer to do its computation in a secure way. You want to mention them. It's going to want to chat with you. It's going to want to join a meeting. To me, all of those embodied agents are seat opportunities.

Yes. And then on the ARPU lift coming from customers embracing M365 and in particular, E5, how far into that opportunity, Rajesh, are we? And if you could remind the audience, is that still a security-led decision given the number of security features you're putting in E5?

The upside on E5 varies vastly by segment. First-line workers, less so, but in the enterprise, in the midsized markets, continue to see opportunity for E5 penetration. Then in terms of is security the biggest driver? I would say, yes. And increasingly, what we call compliance in a world of AI, data classification, data governance has become incredibly important. So the purview aspect of E5 in addition to the defender aspect of E5 is also starting to be a meaningful driver and a pull.

As customers want to go more broadly with AI and agents, they want to get the data governance of their data estate in a way that they can set the right policies for what's accessible to AI and what are the handling policies for AI and which so -- it's security end.

Got it. And Rajesh, you mentioned that now M365 Copilot might rank behind the E5 adoption as key driver of ARPU growth. So given the importance of that, let's talk a little bit about that. Microsoft obviously hasn't yet, at least maybe someday you will, give a metric on Copilot such as seat growth, et cetera. So what's your advice to the audience in terms of what we on the outside can monitor to get some sense of the traction that Copilot is having? Is it more anecdotal comments sort of ranking it as a key driver of ARPU? Is that it? Or is there anything else, Rajesh, you recommend we watch for?

We continue to consider what else we would share and when. But there are a couple of things I can point to. As we recently disclosed, 2 quarters in a row, the daily active engagement with Copilot has more than doubled quarter-over-quarter. That, to me, is a real sign of product with increased intensity of usage and more users engaging with the Copilot experience.

Another one I would point to, maybe a couple of quarters ago, we said, what, 70% of the Fortune 500 had Copilot. Now that's up to 90%. And I do think the ARPU growth is a good one to think about. As we discussed, most of our seat growth today is in lower ARPU base, small businesses, first-line workers. And the fact that our ARPU growth has been stable, Copilot is a contributor to that. So I think the ARPU growth rate is it's a good one to watch. Our engagement rate is a good one to watch.

Okay. That usage improvement, I can tell you, I don't think you were in the audience, but maybe some Microsoft members were. I did a panel this morning with a couple of the lead IT executives at UBS. As you know better than anybody, we've become a very large Copilot customer. So I asked our executives this question. And I think the stuff they shared with me was that UBS employee Copilot usage is up something like 9x on a year-over-year basis. So that's a testimony to what you're describing in terms of usage improvement.

Yes, yes, we see that.

Okay. Is there anything Microsoft can do to help large Copilot customers like UBS drive usage? How is Microsoft helping us get there?

Yes, there's a ton we can do and a ton that we are doing and continuing to invest in, and we learn from our customers what else we have to go do. But let me just talk about a few of those things. The first, of course, is our customer success teams, all the way from deployment to change management, to scenario planning to business value workshop. And by the way, we've decided deliberately to have a bunch of that CSM or customer success management to be core out of the core product team. So the feedback loop between customers and the product team stays super high.

The second one, I would say, as more and more customers, including UBS, are starting to go beyond Copilot as an assistant to agentic workflows. We have FDEs, our forward deployed engineers, again sitting out, of my team, working with customers for the first 5 of the first 10 of these agents that we pick with the customer on either top line drivers or real cost reducers or efficiency or quality drivers. And then we are working with our SI partners so they can scale out and do some of the more -- some of the same.

A third thing that we're building into the product itself is called Copilot Analytics because we want customers to actually be able to measure the ROI and see the business value. So the Copilot Analytics, customers can now do cohort analysis. They can give Copilot to a bunch of users, have a control group and do an agent for one group, not do an agent for the other group. Join the metrics that we see in terms of collaboration and productivity and join that with their own KPIs and see what the ROI is and see what drives the usage at what time across which application. So we want to build more of that stuff in the product itself, and that's what the Copilot Analytics is about.

And finally, I think a big unlock increasingly is going to be agent governance and not just agent governance, broadly governance, even with UBS. Should they enable web grounding for Copilot or should they not enable web grounding for Copilot. So there are a set of controls they would like. When I issue a prompt, what part of the prompt actually leaves the compliance boundary? Do we have the right checks and balances? Because every deployment has to pass risk and governance and security. So we are now year 2 of this thing, and we are getting more and more mature in the governance. And likewise, on agent governance, it's a huge ask from customers.

So I've never seen an announcement in this realm generate the sort of excitement with the IT community that we had at Ignite, which is Agent 365. Because the big worry for customers is, hey, do we democratize the creation of agents? If so, do I have an agent sprawl where it's like the -- all Access database sprawl or VB Macro sprawl. With Agent 365, we give them one observability plane, one data classification plane, one identity plane, whether the agents are built by Microsoft, built by a third party. So we have a lot of partners in the state. So Agent 365 is going to be the other big product investment that we are making that is going to go and enable customers to go bigger with these things.

And Rajesh, in terms of other drivers that might -- the catalyst for unlock needs to come from your partners, not from Microsoft, is the model performance. Yes. I think a lot of us listened to Sam Altman a couple of weeks back and a pod he did. Where he was actually almost admitting that OpenAI needs to make the models even better as an unlock to get more people using AI applications like Copilot. So where do you think we are on the model evolution? And do we need to wait essentially for GPT-6 to get the next big step function improvement in Copilot that would then drive even more usage? How dependent do you think the usage trajectory is on underlying model performance?

I mean, clearly, if the model gets better, the experiences are going to get better. If architected correctly, you want your experiences to be modeled forward. The model gets better, the experiences get better. That's what modern experiences have to be crafted to go to. But I think there are 3 things that are already elevating the capabilities of Copilot and those -- I think the first is the reasoning model finally unlocks AI and graphical user interfaces to work much more effectively.

So I'm very excited in the time -- in the January, February time frame, you're going to see agent mode inside of Office, inside of Excel, inside of Word, inside of PowerPoint, in your calendar, in your meeting, inside your Teams channel. And that is going to be a big one. It's one thing to go and say there's AI off on the side, which I think is a very interesting user experience because you get to express your intent in a natural language. But then to be able to take that same capability and bring that to existing workflows that hundreds of millions of people are already on with a graphical user interface and have those 2 coexist, I think the agent mode is going to be a big unlock. So that's thing one.

The second thing is what we talked about, enabling more agentic workflows. And this is where Agent 365 as a governance and a compliance plane enables customers to go without the worry of a sprawl or a management issue on their hands. And finally, with Copilot, we brought multiple sources of intelligence into Copilot. Just like if I hire an employee with liberal arts background to work on a project and I hire an employee with a STEM background to work on a project, they're going to bring different strengths. And so with Copilot, it's not just about the model. It's about how the models come together with the graphical user interface, the innate capabilities of the model, multiple lineages of the model to actually deliver the outcome or the workflow that you're planning to go do, whether in our applications or agents that you will build on your own.

So let's press maybe a little bit on the enterprise Agentic adoption, which you're describing as some interesting things coming that could be an unlock. So Rajesh, we do talk to some people who are more at the skeptical end of the spectrum where they say that it's going to be a long haul to get companies really embracing agents, especially agents acting relatively autonomously. You sound a lot more optimistic. What's the gap here that gives you that confidence? And where do you think generally enterprises are in investing heavily in Agentic apps?

I mean for sure, I mean, I will agree with the fact that it's not a uniform thing. I mean any enterprise -- any new technology goes through its enterprise curve with the risk and management, ROI analysis, business value, all of that stuff. That being said, let me just say, I mean, the word agent is so overused. I'd like to think about agents as 3 -- think of a spectrum. On one end of the spectrum, you've got what used to traditionally be an end user going and creating a great template or an access database or an Excel, if I'm a lawyer doing IPE rebuttals, I have a 20-page contract as a template that my work group uses. Those things can be actually implemented very capably as an agent, built at the departmental level by a power user and that kind of a thing today has been really bottled up by a lot of customers because of the governance issue. And the Agent 365 really unlocks that because now you have governance, you have -- you can see what's been shared by who, how often is it used, what should get aged out.

So that's one end of the spectrum. And by the way, companies are going to have to enable that because no top-level IT or business leader of a large organization have visibility into the workflows that impact the daily lives of a lot of information workers. So anyway, that's one end of the spectrum. The middle end of the -- in the spectrum is classic high-value applications. And these are applications that might be really important from a cost perspective, revenue perspective, quality perspective. And then agents are a great implementation. Take the GenAI engine, get the right grounding, right enterprise policies and you go enable that.

Now let me give you an example of such a thing that we talked about at Ignite finally getting to general availability with huge excitement by our customers. It's called employee self-service. Think of it as a template for an agent. Every customer that we talk to, every large organization has help desk for IT. They have HR tickets coming in. They have people making requests for conference rooms, event management, all of those things. And so this is broadly applicable having an agentic workflow that can take all your business policies, quickly be able to tell you, hey, your kid is getting 26 that need to come off your medical plan. It is trivial for such an agent to get the right one click, let me go take care of that when you go to this agent.

But this is not a simple agent. And I was telling somebody like if you're an employee and you ask, hey, what's the paternity leave policy and you happen to be in Norway, HR is very particular for that particular thing. There needs to be no AI. It needs to be the authoritative HR policy that needs to be given back to the employee. If somebody files a ticket saying, "Hey, I'm depressed, I'm looking for resources." You don't want HR adjudicating. You want that employee to be connected to a human.

So the ESS is kind of a broadly applicable app pattern, agent pattern that we are starting to go build to enable and bootstrap this kind of an agent deployment. And then the other end of the spectrum of agents are digital workers. They're going to show up in your org chart. They're going to show up in a meeting roster. They're going to show up as a part of the channel member. You're going to mention somebody who's a new digital marketing employee in your marketing group to go do research and give you back a document that they post back into the Teams channel. That is probably the most immature end, but 2026, you're going to start to see those things happen as well.

Okay. That's exciting. Let me ask you a couple of more questions about Copilot, then we'll move on to other parts of your portfolio. One of the interesting announcements that Microsoft made recently was that, well, Copilot has to date been run largely on OpenAI models. You announced a partnership where customers can access essentially an Anthropic version of Copilot. Can you describe that decision? What's the thinking behind that?

Yes. Again, I mean, with Copilot, our goal here is to make sure you can get to the outcome that you were trying to get done as an information worker or in the context of a business process. And we noticed that the Anthropic model does a pretty good job with different perspective. If I ask OpenAI model to do research on a given topic, it tends to be well written, well crafted. If I ask the Anthropic model to do the same thing, I'm going to see many more charts, it's going to be much more concise, neither is better than the other.

And so I want to give a choice to the user as to what works best for them. And they could run both, pick the one that they want. And so it's no different than like I said, if you hire 2 employees in your work group and they come from 2 different backgrounds, 2 different schools, 2 different skill sets, they're going to do different -- they're going to do very competent work, but it's going to be different. And so for us, it's about the best possible outcomes.

And by the way, it's not just the Anthropic model, the OpenAI model. I think one of the more interesting things that we've seen customers go do is if you have a business process that is unique to you, that is your differentiated IP as a financial services and insurance company as a pharmaceutical company, you want the model to be tuned to your IP with the weights belonging to you, not the weights collapsing into the model.

And so that's what Copilot tuning is about. We'll let customers take a base model, tune it with no Microsoft eyes on it with their subject matter experts, create a fine-tuned model, measure the efficacy of the model against the base model and then deploy it back into the Copilot experiences that our employees are going to go use. So it's less about the model. It's making sure that the entire experience is optimized for the outcomes you're trying to drive.

Okay. Last one I want to ask you on Copilot is around competition. So I'm sure when you and the leadership team launched M365 Copilot, you were under no illusion that you would have that market to yourself. It's always competitive in every corner of the software space. But your -- one of your key partners, OpenAI now is trying to scale into their targets, partly on the back of ChatGPT for enterprise, which many organizations might use alongside Copilot, maybe in some cases, instead of. Google with Gemini now is pushing Gemini for enterprise. So getting a little bit more competitive. What are your thoughts, Rajesh, on the ability of Microsoft to stay ahead of those 2 rivals in particular?

It's a great question. So let me first just say, look, I mean, with -- let me talk about Copilot. What's differentiated, what's unique. The first thing we got to do is to make sure we meet users' expectations with what they expect generative AI to do, be a great writer, be a great analyzer, be able to tell you the weather, create an image and all of that stuff. So that's table stakes. We're going to go do that, and we're going to be competitive like any other Gen AI tool on that. Now let's talk about what's unique and what's differentiated. The first thing is what we call Work IQ, which is understanding your work context.

Most of the people powering economic activity around the globe are on M365. So Work IQ gives us a pretty good understanding of the projects that matter to you, who your work group is, what events are interesting, who are your customers, your e-mails, your meetings, your documents. We have a pretty good understanding of that stuff. Then you'll hear the other tools talk about connectors. Connector is trying to drink through a very thin straw and understand your work context. Now you can take many sips from a thin straw, it is still a thin straw. And so we have customers who've done side-by-side analysis of Copilot in the context of your work and connector-based things and the gap is very significant and growing.

So anyway, that's just thing one. Thing two, though, as people use Copilot both as a new endpoint as a peer to what Excel, PowerPoint, Outlook, Teams, as a pure endpoint. But they have billions of transactions every hour that go through these applications. We have an opportunity to serve the same Work IQ intelligence with agent mode inside of these existing applications that people use, we can uniquely do that well. That's thing two.

Thing three, human-to-human collaboration, communication, productivity, highly regulated. We have 15 years' experience on how to do eDiscovery, sensitivity label, all sorts of things, legal hold, data classification, data governance. All of that works with human to AI conversations in Copilot automatically for customers. Our 15 years of maturity on that stuff is enterprise-grade maturity and human-to-human productivity. Human and AI productivity gets all of that for free. The other providers are going to have to go build all of that capability. Thing 4, can I keep going or you can keep coming.

Yes. So thing 4, though, it's like can I go back to we are not beholden to a model. We are beholden to the best outcome. And so we will build experiences that are powered by whether it be our IP partner, OpenAI, whether it be Anthropic, whether it be copilot tuning, whether it be an open source model whose weights you want to go create as your own unique IP. So we are multi-model.

Finally, the more important thing that I go back to the comment I made earlier, if you think that the future workforce is going to have more agents than humans, and this is going to be true in every organization. How is the human supposed to get work? I thought that the beauty of AI was I don't have to worry about tools anymore. I have one natural language interface that abstracts away the tools, and I express my intent.

In a world where now I'm surrounded by hundreds of agents that are semiautonomous, some that I'm managing, some that I have to rendezvous with, how does a human navigate all of that stuff? Copilot is going to be the UI through which they orchestrate the agent. We are going to be the search engine for these agents. We're going to be the relevance engine for these agents. We're going to be the orchestration engine for these agents. IT is going to have a bunch of controls. Karl, you're in this department. The following agents are the most relevant to you. You work group created an agent that you tend to use a lot. Copilot will know to use that. So when you put all of these things together, I really like our ability to go serve M365 customers with M365 Copilot.

Yes. Rajesh, I had so many other questions on other parts of your portfolio like Teams and Windows that I don't even have time for. But I noticed we have 2 minutes left, and I do want to get to one last one that I know is of interest to this group. And that is the compute capacity. So Microsoft obviously needs to stand up an enormous amount of compute capacity for multiple constituents. You've obviously got partners like OpenAI that have demands on you. You've got enterprise customers like UBS that over time, if not today, need Azure-based compute capacity.

Absolutely.

But this conversation is about really Microsoft's first-party applications because you need to ensure that the Copilot and Agentic AI experience is robust. So you need to make sure that you've got enough compute capacity to serve your first-party apps. So I guess the question is, how big a constraint is that? How big of a problem is it given that Microsoft, as Amy described to all of us, is very compute constrained right now. So how do you ensure, Rajesh, that you knock on Amy's door and make sure that she's carving out sufficient compute for your business?

Yes. You're right, Karl. I mean this is a multidimensional thing that we spend a lot of time on. But holistically, it's what you would expect. We take a left to right view across our third-party commitments, our first-party commitments. Inside of the first-party commitments, clearly, M365 Copilot, GitHub Copilot, these are the huge priorities. But it isn't as simple as which is priority 1, which is priority 2. There are multiple dimensions to this. I mean, not all scenarios need all sorts of hardware. Not all load happens at the same time across the globe.

We have different geo ring-fencing constraints in different parts of our businesses. Saturday morning peak, Saturday, 3:00 a.m. is our lowest use time. What parts of the AI can be done asynchronously during that time? How much latency is affordable for which segment? How much optimization can we bring out, not just at the chip level, but in the global routing level to optimize for all of these things. And so we work this multidimensional thing even as we add more and more capacity, and we look left to right against all the prioritization. So suffice it to say, this is top of mind, and I feel good about the process we have in place to meet all the stakeholders and navigate that.

Got it. Okay. I think that's all the time we have. Rajesh, I learned a lot. Thank you. And I'm very proud to work for what's now become a pretty significant Microsoft Copilot and Azure customer in...

Keep the feedback coming, Karl.

Thank you. Thank you so much.

Thank you, Karl.

Greetings, and welcome to the Microsoft Fiscal Year 2026 First Quarter Earnings Conference Call. [Operator Instructions] As a reminder, this conference is being recorded.

It is now my pleasure to introduce Jonathan Neilson, Vice President of Investor Relations. Please go ahead.

Good afternoon, and thank you for joining us today. On the call with me are Satya Nadella, Chairman and Chief Executive Officer; Amy Hood, Chief Financial Officer; Alice Jolla, Chief Accounting Officer; and Keith Dolliver, Corporate Secretary and Deputy General Counsel.

On the Microsoft Investor Relations website, we will provide our earnings press release and financial summary slide deck, which is intended to supplement our prepared remarks and provides the reconciliation of differences between GAAP and non-GAAP financial measures. More detailed outlook slides will be available on the Microsoft Investor Relations website.

On this call, we will discuss certain non-GAAP items. The non-GAAP financial measures provided should not be considered as a substitute for, or superior, to the measures of financial performance prepared in accordance with GAAP. They are included as additional clarifying items to aid investors in further understanding the company's first quarter performance in addition to the impact these items and events have on the financial results.

All growth comparisons we make on the call today relate to the corresponding period of last year, unless otherwise noted. We will also provide growth rates in constant currency when available as a framework for assessing how our underlying business performed, excluding the effect of foreign currency rate fluctuations. Where growth rates are the same in constant currency, we will refer to the growth rate only.

We will post our prepared remarks to our website. Today's call is being recorded. If you ask a question, it will be included in our live transmission, in the transcript and in any future use of the recording. You can replay the call and view the transcript on the Microsoft Investor Relations website.

During this call, we will be making forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could materially differ because of factors discussed in today's earnings press release, in the comments made during this conference call and in the Risk Factors section of our Form 10-K, Forms 10-Q and other reports and filings with the Securities and Exchange Commission. We do not undertake any duty to update any forward-looking statements.

And with that, I'll turn the call over to Satya.

Thank you, Jonathan. It was a very strong start to our fiscal year. Microsoft Cloud revenue surpassed $49 billion, up 26% year-over-year. And our commercial RPO increased over 50% to nearly $400 billion with a weighted average duration of only 2 years. We are seeing increasing demand and diffusion of our AI platform and family of Copilots, which is fueling our investments across both capital and talent.

When it comes to infrastructure, we're building a planet scale cloud and an AI factory, maximizing tokens per dollar per watt while supporting the sovereignty needs of customers and countries. We are innovating rapidly across the family of Copilots spanning the high-value domains of information work, coding, security, science, health and consumer.

And as you saw yesterday, we closed a new definitive agreement with OpenAI, marking the next chapter in what is one of the most successful partnerships and investments our industry has ever seen. This is a great milestone for both companies and we continue to benefit mutually from each other's growth across multiple dimensions. Already, we have roughly 10x-ed our investment. OpenAI has contracted an incremental $250 billion of Azure services, our rev share, exclusive IP rights and API exclusivity for Azure continue until AGI or through 2030. And we have extended the model and product IP rights through 2032.

And we are also energized to innovate and pursue AI advancements with both talent and compute investments that have real-world impact. With that, let's turn to our momentum across our AI platform and Copilots as well as with agents.

We have the most expansive data center fleet for the AI era, and we are adding capacity at an unprecedented scale. We will increase our total AI capacity by over 80% this year and roughly double our total data center footprint over the next 2 years, reflecting the demand signals we see. Just this quarter, we announced the world's most powerful AI data center, Fairwater in Wisconsin, which will go online next year and scale to 2 gigawatts alone.

And we have deployed the world's first large-scale cluster of NVIDIA GB300s. We are building a fungible fleet that's been continuously modernized and spans all stages of the AI life cycle from pretraining to post training to synthetic data generation and inference. And it also goes beyond GenAI workloads to recommendation engines, databases and streaming. We're optimizing this fleet across silicon systems and software to maximize performance and efficiency.

It's this combination of fungibility and continuous optimization that allows us to deliver the best ROI and TCO for us and our customers. For example, during the quarter, we increased the token throughput for GPT-4.1 and GPT-5, two of the most widely used models by over 30% per GPU. We also have the most comprehensive digital sovereignty platform. Azure customers in 33 countries are now developing their own cloud and AI capabilities within their borders to meet local data residency requirements.

In Germany, for example, OpenAI and SAP will rely on Azure to deliver new AI solutions to the public sector. On top of this infrastructure, we are building Azure AI Foundry to help customers build their own AI apps and agents. We have 80,000 customers, including 80% of the Fortune 500. We offer developers and enterprise access to over 11,000 models more than any other vendor, including as of this quarter, OpenAI's GPT-5 as well as xAI's Grok 4. For example, Ralph Lauren used Foundry to build conversational shopping experience in its app, enabling customers to describe what they're looking for and get personalized recommendations. And OpenEvidence used Foundry to create its AI-powered clinical assistant which surfaces relevant medical information to physicians and help streamline charting.

When it comes to our first-party models, we are excited by the performance of our new MAI models for text, voice and image generation, which debuted among the top in the industry leader boards. And we continue to make great progress with our Phi family of SLMs, which now have been downloaded over 60 million times, up 3x year-over-year.

Beyond models in Foundry, we are providing everything developers need to design, customize and manage AI applications and agents at scale. Our new Microsoft Agent Framework helps developers orchestrate multi-agent systems with compliance, observability and deep integration out of the box. For example, KPMG used the framework to modernize the audit process, connecting agents to internal data with enterprise-grade governance and observability. These kinds of real production scale AI deployments are driving Azure's overall growth. And once again, this quarter, Azure took share.

Now let's turn to applications and agents we ourselves are building on this platform. We now have 900 million monthly active users of our AI features across our products. And our first-party family of Copilots now has surpassed 150 million monthly active users across the information work, coding, security, science, health and consumer.

When it comes to information work, we continue to innovate with Microsoft 365 Copilot. Copilot is becoming the UI for the agentic AI experience. We have integrated chat and agentic workflows into everyday tools like Outlook, Word, Excel, PowerPoint and Teams. Just 9 months since release, tens of millions of users across Microsoft 365 customer base are already using chat. Adoption is accelerating rapidly, growing 50% quarter-over-quarter, and we continue to see usage intensity increased.

This quarter, we also introduced Agent Mode, which turns single prompts into export quality Word documents, Excel spreadsheets, PowerPoint presentation and then iterate to deliver the final product much like agent mode in coding tools today. We're thrilled by the early response, including third-party benchmarks that rank it best-in-class.

Beyond individual productivity, Copilot is multiplayer, with Teams mode announced this week, you can now invite colleagues into a Copilot conversation. And our collaborative agents like Facilitator and Project Manager, prep meeting agendas, take notes, capture decision, kick off group tasks.

We are seeing a growing Copilot agent ecosystem with top ISVs like Adobe, Asana, Jira, LexisNexis, SAP, ServiceNow, Snowflake and Workday, all building their own agents that connect to Copilot. And customers are also building agents for their mission-critical business processes and workflows using tools like Copilot Studio and integrating them into Copilot. The overall number of agent users doubled quarter-over-quarter. And just yesterday, we announced App Builder, a new Copilot agent that lets anyone create and deploy task-specific apps and agents in minutes grounded in Microsoft 365 context.

All this innovation is driving our momentum. Customers continue to adopt Microsoft 365 Copilot at a faster rate than any other new Microsoft 365 suite, all up more than 90% of the Fortune 500 now use Microsoft 365 Copilot, Accenture, Bristol-Myers Squibb, EY Global and the U.K.'s Tax and Payments and Customs Authority all purchased over 15,000 seats this quarter. Lloyds Banking Group has deployed 30,000 seats, saving each employee an average of 46 minutes daily.

And a large majority of our enterprise customers continue to come back to purchase more seats. Our partner, PwC, alone added 155,000 seats this quarter and now has over 200,000 deployed across its global operations. In just 6 months, PwC employees interacted with Microsoft 365 Copilot over 30 million times, and they credit this agentic transformation with saving millions of hours an employee productivity.

When it comes to coding, GitHub Copilot is the most popular AI pair programmer now with over 26 million users. For example, tens of thousands of developers at AMD use GitHub Copilot, accepting hundreds of thousands of lines of code suggestions each month and crediting it with saving months of development time. All up, GitHub is now home to over 180 million developers and the platform is growing at the fastest rate in its history, adding a developer every second. 80% of new developers on GitHub start with Copilot within the first week.

Overall, the rise of AI coding agents is driving record usage with over 500 million pull requests merged over the past year. And just yesterday, at GitHub Universe, we introduced Agent HQ. GitHub Copilot and Agent HQ is the organizing layer for all coding agent, extending GitHub privatives like PRs, issues, actions to coding agents from OpenAI, Anthropic, Google, Cognition, xAI as well as OSS and in-house models. GitHub now provides a single mission control to launch, manage and review these agents, each operating from its own branch with built-in controls, observability and governance.

We are building a similar system in security with over 3 dozen agents in Copilot integrated across Entra, Defender, Purview and Intune. For example, with our Phishing Triage Agent in Defender, studies show that analysts can be up to 6.5x more efficient in detecting malicious mails.

In health, Dragon Copilot helps providers automate critical workflows. This quarter alone, we helped document over 17 million patient encounters, up nearly 5x year-over-year. More than 650 health care organizations have purchased our ambient listening tech to date, including University of Michigan Health where over 1,000 physicians are actively using it.

Finally, when it comes to AI consumer experiences, we are excited about all the progress Copilot is making, starting with Windows. Every Windows 11 PC now is an AI PC. Just 2 weeks ago, we introduced new ways to speak naturally to your computer, including a Copilot wake word. With vision, Copilot sees what you see on your screen and you can have a real-time conversation about it. And with action, it takes real action on your behalf, interacting with both web and desktop apps.

In Edge, we are introducing first-of-its-kind AI features to automate multistep workflows within the browser and help you pick up right where you left off. Edge now has taken share for 18 consecutive quarters. In Bing, our overview pages now include embedded conversational capabilities, we took share again in search. And daily users of our Copilot consumer app increased nearly 50% quarter-over-quarter. Among many updates we made last week is groups, which turns Copilot for the first time into a shared experience.

We also are creating a great consumer subscription offer with Microsoft 365 premium. It brings together our Office applications and advanced Copilot features with high usage limits, giving individuals the flexibility to bring their own AI to work in a secure way.

Finally, in gaming, Copilot provides a voice-first immersive experience across PC, mobile and our new Xbox Ally.

Beyond our family of Copilots and AI platform, we are seeing strong momentum across the portfolio. Cloud migrations are accelerating. In data and analytics, Fabric revenue grew 60%, which is faster than any other data and analytics platform in the industry. We now have 28,000 paid Fabric customers. In databases, SQL, DB, hyperscale revenue was up nearly 75%, 50% in Cosmos DB. In Business Applications, Dynamics 365 gained share. In security, our end-to-end stack is now informed by 100 trillion daily signals. 1 billion monthly active users of Entra, 16 billion Copilot interactions audited by Purview, up 72% quarter-over-quarter. 40,000 Sentinel customers, and we took share across all categories we serve in security. In LinkedIn, nearly 1.3 billion members.

And finally, in gaming, we expanded our reach across every endpoint focused on our high-margin content and services. We launched critically acclaimed games like Keeper, Ninja Gaiden 4 and Outer Worlds 2, reaching 155 million monthly active users. Minecraft, an all-time high, and set new record for overall content and services revenue for the quarter. We also saw a great response to Xbox Ally launch 2 weeks ago and set new records for players on PC.

In closing, our planet-scale Cloud and AI factory together with Copilots across high-value domains is driving broad diffusion and real-world impact. And we continue to increase our investments in AI across both capital and talent to meet the massive opportunity ahead.

With that, let me turn it over to Amy to walk through our financial results and outlook, and I look forward to rejoining for your questions.

Thank you, Satya, and good afternoon, everyone. First, as you heard from Satya, we were pleased to announce the next phase of our partnership with OpenAI yesterday. They continue to choose Microsoft to power their workloads. And together, we remain committed to driving innovation that meets real-world needs. Our Q1 results were not impacted by the deal signed this week.

Now on to the quarter, we delivered a strong start to our fiscal year, exceeding expectations across revenue, operating income and earnings per share. We also saw continued share gains across many of our businesses, demonstrating our leadership position in key markets.

This quarter, revenue was $77.7 billion, up 18% and 17% in constant currency. Gross margin dollars increased 18% and 16% in constant currency, while operating income increased 24% and 22% in constant currency. And earnings per share was $4.13 and an increase of 23% and 21% in constant currency when adjusted for the impact of our investments in OpenAI. FX impact was roughly in line with guidance.

Company gross margin percentage was 69%, down slightly year-over-year, driven by investments in AI, including the impact of scaling our AI infrastructure and the growing usage of our AI product features. This was partially offset by ongoing efficiency gains, particularly in Azure and M365 Commercial cloud.

Operating expenses increased 5% and 4% in constant currency, driven by investments in Cloud and AI engineering, including compute capacity and AI talent to support product development across the portfolio. Operating margins increased year-over-year to 49% and were ahead of expectations with stronger-than-anticipated results and high-margin businesses this quarter.

When adjusted for the impact from our investments in OpenAI, other income and expense was $401 million as interest income more than offset interest expense, which includes the interest of payments related to data center finance leases.

Capital expenditures were $34.9 billion, driven by growing demand for our Cloud and AI offerings. This quarter, roughly half of our spend was on short-lived assets, primarily GPUs and CPUs, to support increasing Azure platform demand, growing first-party apps at AI solutions, accelerating R&D by our product teams as well as continued replacement for end-of-life server and networking equipment.

The remaining spend was for long-lived assets that will support monetization for the next 15 years and beyond, including $11.1 billion of finance leases that are primarily for large data center sites. And cash paid for PP&E was $19.4 billion. As a reminder, the difference between total CapEx and cash paid for PP&E is primarily due to finance leases as well as the normal timing of goods received, but not yet paid.

Cash flow from operations was $45.1 billion, up 32%, driven by strong Cloud billings and collections, partially offset by higher supplier payments. And free cash flow increased 33% to $25.7 billion with minimal impact from a sequential increase in CapEx, given the higher mix of finance leases.

And finally, we returned $10.7 billion to shareholders through dividends and share repurchases.

Now to our commercial results. Commercial bookings increased 112% and 111% in constant currency and were significantly ahead of expectations, driven by Azure commitments from OpenAI as well as continued growth in the number of $100 million-plus contracts for both Azure and M365. These results do not include any impact from the incremental $250 billion Azure commitments from OpenAI announced yesterday.

Commercial remaining performance obligation increased to $392 billion and was up 51% year-over-year. The balance has nearly doubled over the past 2 years. And even with this growth, our weighted average duration has been relatively stable at approximately 2 years.

Microsoft Cloud revenue was $49.1 billion, ahead of expectations and grew 26% and 25% in constant currency. Microsoft Cloud gross margin percentage was slightly better than expected at 68% and down year-over-year due to the investments in AI that were partially offset by ongoing efficiency gains as noted earlier.

Now to segment results. Revenue from Productivity and Business Processes was $33 billion and grew 17% and 14% in constant currency. M365 Commercial Cloud revenue increased 17% and 15% in constant currency with 1 point of benefit from in-period revenue recognition. Year-over-year growth was driven by both ARPU and seats with ARPU growth again led by E5 and M365 Copilot. Paid M365 Commercial seats grew 6% year-over-year with installed base expansion across all customer segments though primarily in our small and medium businesses and frontline worker offerings.

M365 Commercial Products revenue increased 17% and 14% in constant currency, ahead of expectations due to higher-than-expected Office 2024 transactional purchasing. M365 Consumer Cloud revenue increased 26% and 25% in constant currency, again driven by ARPU growth. M365 consumer subscriptions grew 7% to over 90 million.

LinkedIn revenue increased 10% and 9% in constant currency driven by Marketing Solutions. The Talent Solutions business was impacted by continued weakness in the hiring market. Dynamics 365 revenue increased 18% and 16% in constant currency with continued growth across all workloads.

Segment gross margin dollars increased 19% and 16% in constant currency, and gross margin percentage increased driven by efficiency gains in M365 Commercial Cloud that were partially offset by investments in AI including the impact of growing usage in M365 Copilot chat. Operating expenses increased 6% and 5% in constant currency, and operating income increased 24% and 20% in constant currency. Operating margins increased 3 points year-over-year to 62%, driven by the higher gross margin noted earlier as well as improved operating leverage.

Next, the Intelligent Cloud segment. Revenue was $30.9 billion and grew 28% and 27% in constant currency. In Azure and other Cloud services, where we continue to see accelerating demand, revenue grew 40% and 39% in constant currency. Results were ahead of expectations, driven by better-than-expected growth in our core infrastructure business, primarily from our largest customers.

Azure AI services revenue was generally in line with expectations, and this quarter, demand again exceeded supply across workloads, even as we brought more capacity online.

In our on-premise server business, revenue increased 1% and was relatively unchanged in constant currency. Results were ahead of expectations, driven by transactional purchasing of Windows Server 2025. Segment gross margin dollars increased 20% and 19% in constant currency, and gross margin percentage decreased year-over-year, driven by investments in AI that were partially offset by efficiency gains in Azure. Operating expenses increased 4% and operating income grew 27%. Operating margins were 43%, down only slightly year-over-year as increased investments in AI were mostly offset by improved operating leverage.

Now to More Personal Computing. Revenue was $13.8 billion and grew 4%. Windows OEM and Devices revenue increased 6% year-over-year, significantly ahead of expectations, driven by strong demand ahead of Windows 10 end of support as well as a benefit from inventory levels that remain elevated.

Search and news advertising revenue ex TAC increased 16% and 15% in constant currency, driven by growth in volume as well as the continued benefit from third-party partnerships that was better than expected. And in gaming, revenue decreased 2% and 3% in constant currency against a strong prior year comparable. Xbox content and services revenue increased 1% and was relatively unchanged in constant currency, driven by better-than-expected performance from third-party content.

Segment gross margin dollars increased 11% and 10% in constant currency, and gross margin percentage increased year-over-year driven by sales mix shift to higher-margin businesses. Operating expenses increased 4% and 3% in constant currency, and operating income increased 18% and 16% in constant currency. Operating margins increased 3 points year-over-year to 30% driven by the higher gross margin noted earlier.

Now moving to our Q2 outlook, which unless specifically noted otherwise, is on a U.S. dollar basis. Based on current rates, we expect FX to increase total revenue growth by 2 points. Within the segments, we expect FX to increase revenue growth by 2 points in Productivity and Business Processes and Intelligent Cloud and 1 point in More Personal Computing. We expect FX to increase COGS and operating expense growth by 1 point.

Starting with the total company, we expect revenue of USD 79.5 billion to USD 80.6 billion or growth of 14% to 16%. We expect COGS of USD 26.35 billion to USD 26.55 billion or growth of 21% to 22%. And operating expense of USD 17.3 billion to USD 17.4 billion, growth of 7% to 8%. Operating margins should be relatively flat year-over-year and down sequentially, aligned with historic seasonality.

Now other income and expense. The combination of OpenAI's conversion to a public benefit corp and the ongoing nature of our partnership will result in increased volatility. Therefore, going forward, we'll provide our outlook, excluding any impact from our investments in OpenAI. On that basis, in Q2, other income and expense is estimated to be roughly $100 million as interest income will more than offset interest expense. And we expect our Q2 effective tax rate to be approximately 19%.

Next, capital expenditures. With accelerating demand and a growing RPO balance, we're increasing our spend on GPUs and CPUs. Therefore, total spend will increase sequentially, and we now expect the FY '26 growth rate to be higher than FY '25. As a reminder, there can be quarterly spend variability from cloud infrastructure build-outs and the timing of delivery of finance leases.

Next, our commercial business. In commercial bookings, we expect healthy growth in the core business on a low expiry base when adjusted for the OpenAI contracts in the prior year. And we expect commercial bookings will be positively impacted by the significant OpenAI commitments announced yesterday. As a reminder, larger long-term Azure contracts, which are more unpredictable in their timing, drive increased quarterly volatility in our bookings growth rate.

Microsoft Cloud gross margin percentage should be roughly 66%, down year-over-year, driven by the continued investments in AI as well as the mix shift to Azure.

Now to segment guidance. In Productivity and Business Processes, we expect revenue of USD 33.3 billion to USD 33.6 billion or growth of 13% to 14%. In M365 Commercial Cloud, we expect revenue growth to be between 13% and 14% in constant currency, with business trends that remain relatively stable quarter-over-quarter. ARPU growth will again be driven by E5 and M365 Copilot. M365 Commercial products revenue growth should be in the low to mid-single digits. As a reminder, M365 Commercial products includes components that can be variable due to the in-period revenue recognition dynamics.

M365 Consumer cloud revenue growth should be in the mid-20s driven by growth in ARPU. For LinkedIn, we expect revenue growth of approximately 10%. And in Dynamics 365, we expect revenue growth to be in the mid- to high teens with continued growth across all workloads.

For Intelligent Cloud, we expect revenue of USD 32.25 billion to USD 32.55 billion or growth of 26% to 27%. In Azure, we expect Q2 revenue growth of approximately 37% in constant currency as demand remains significantly ahead of the capacity we have available. And while we're accelerating the amount of capacity we're bringing online, we will continue to balance Azure revenue growth with the growing needs across our first-party apps and AI solutions, our own R&D efforts and the end-of-life server replacements. Therefore, we now expect to be capacity constrained through at least the end of our fiscal year.

As a reminder, there can be quarterly variability in the year-on-year growth rates depending on the timing of capacity delivery and when it comes online as well as from in-period revenue recognition depending on the mix of contracts.

In our on-premises server business, we expect revenue to decline in the low to mid-single digits with ongoing customer shift to cloud offerings.

In More Personal Computing. We expect revenue to be in the USD 13.95 billion to USD 14.45 billion. Windows OEM and Devices revenue should decline in the mid-single digits. We expect continued momentum from Windows 10 end of support, although growth rates will be impacted by elevated inventory levels at the end of Q1 that we expect to come down through the quarter. Therefore, Windows OEM revenue should decline low to mid-single digits. The range of potential outcomes remains wider than normal. Devices revenue should decline year-over-year. Search and news advertising ex TAC revenue growth should be in the low double digits, down sequentially as growth rates normalize following the benefit from third-party partnerships noted earlier. Growth will continue to be driven by volume and revenue per search across Edge and Bing.

And in Xbox content and services, we expect revenue to decline in the low to mid-single digits against the prior year comparable that benefited from strong first-party performance, partially offset by growth in subscriptions. And hardware revenue should decline year-over-year.

And in closing, demand signals across bookings, RPO and product usage are accelerating faster than we expected. We're investing in infrastructure, AI talent and product innovation to capture that momentum and expand our leadership position. And we remain focused on delivering real value to our customers that results in durable revenue growth for the long term.

With that, let's go to Q&A, Jonathan.

Thanks, Amy. We'll now move over to Q&A. [Operator Instructions]. Operator, can you please repeat your instructions?

[Operator Instructions] Our first question comes from the line of Keith Weiss with Morgan Stanley.

Congratulations on another outstanding quarter. And if I'm looking at Microsoft, this is 2 quarters in a row, we're really seeing results that are well ahead of anybody's expectations when we were thinking about this company a year ago or 5 years ago, 111% in Commercial bookings growth was not on anybody's bingo card, if you will, yet the stock is underperforming in the broader market. And the question I have is kind of getting at the zeitgeist that I think is weighing on the stock. And is something about to change?

And I think AGI is kind of a nomenclature or a shorthand for that. And it's something that still included in your guys' OpenAI agreement. So Satya, when we think about AGI or we think about how application and computing architectures are changing, is there anything that you see on the horizon, whether it's AGI or something else, that could potentially change what appears to be a really strong positioning for Microsoft in the marketplace today where that strength will perhaps weaken on a go-forward basis. Is there anything that you're worrying about in that evolution and particularly the evolution of these generative AI models.

Thank you, Keith, for the question. So here's what I would say, I think there are 2 parts. We feel very, very good about even this, I'd say, the new agreement that we now have with OpenAI because I think even, it just creates more certainty to all of the IP relationship we have as it relates to even this definition of AGI. But beyond that, I think your question touches on something that's pretty important, which is how are these AI systems going to truly be deployed in the real world and make a real difference and make a return for both the customers who are deploying them and then obviously, the providers of these systems. And I think the best way to characterize the situation is that even as the intelligence capability increases, let's even say, exponentially like model version over model version, the problem is it's always going to still be jagged, right? I think the term people use is the jagged intelligence, even -- or spiky intelligence, right?

So you may even have a capability that's fantastic at a particular task, but it may not uniformly grow. So what is required is in fact, these systems, whether it is GitHub Agent HQ or the M365 Copilot system. Don't think of this as a product. Think of it as a system that in some sense smooths out those jagged edges, and really helps the capability.

I mean just to give you a flavor for it, right? So if I am in M365 Copilot, I can generate an Excel spreadsheet. The good news is now an Excel spreadsheet does understand Office JS, has the formulas in it. It feels like, wow, it is a great spreadsheet created by a good model. The more interesting thing is I can go into agent mode in Excel and iterate on that model. And yet, it will stay on rail. It won't go off rail, it will be able to do the iteration. Then I can even give it to the analyst agent, and then it will even make sense of it like a data analyst would of our Excel model.

The reason I say all of that is because that's the type of construction that will be needed even when the model is magical, all powerful. I think we will be in this jagged intelligence phase for a long time. So one of the fundamental things that these -- whether it's GitHub, whether it's security, whether it's M365, the 3 main domains we're in, we feel very, very good about building these as organizing layers for agents to help customers.

And by the way, that's the same thing that we want to put into Foundry for our third-party customers. So that's kind of how people will build these multi-agent systems. So I feel actually pretty good about both the progress in AI. I don't think AGI as defined at least by us in our contract is ever going to be achieved anytime soon. But I do believe we can drive a lot of value for customers with advances in AI models by building these systems. So it's kind of the real question that needs to be well understood. And I feel very, very confident about our ability to make progress.

The next question comes from the line of Brent Thill with Jefferies.

Amy, on the bookings blowout. I guess many are somewhat concerned about concentration risk. And I think you noted a number of $100 million contracts, not to go into a lot of detail, but can you just give us a sense of what you're seeing in that 51% RPO and 110-plus percent bookings growth that gives you confidence about what you're seeing in terms of the breadth and extent of some of these deals on a global basis.

Thanks, Brent. A couple of things to maybe take a step back on RPO. With a nearly $400 billion balance, we've been trying to help people understand sort of how to think about really the breadth of that. It covers numerous products. It covers customers of all sizes. It -- that's been a balance that we've been growing obviously at a good clip. But what people need to realize is it sits across multiple products because of the things Satya is talking about around creating systems and where we're investing.

And if you're going to have that type of balance and then more importantly, have the weighted average duration be 2 years, it means that most of that is being consumed in relatively short order. People are not consuming, and I say this broadly, unless there's value. And I think this is why we keep coming back to, are we creating real-world value in our AI platforms, in our AI solutions and apps and systems.

And so I think the sort of the way to think about RPO is it's been building across a number of customers. We're thrilled to have OpenAI be a piece of that. We're learning a ton and building leading systems because of it that are being used at scale that benefits every other customer. And so it's why we've tried to give a little bit more color to that RPO balance because I do understand that there have been a lot of concerns or questions about is it long dated, is it coming over a long period of time. And hopefully, this is helpful for people to realize that these are contracts being signed by customers who intend to use it in relatively short order. And at that type of scale, I think that's a pretty remarkable execution.

The next question comes from the line of Mark Moerdler with Bernstein Research.

Congratulations on the quarter. It's pretty amazing what you guys are doing. Satya and Amy, I'd like to ask you the #1 question I receive, whether from investors or at AI conferences I attend, how much confidence do you have that the software, even the consumer [indiscernible] business can monetize all the investments we're seeing globally? Or frankly, are we in a bubble? In fact, Amy, what would be the factors you'd be watching for to assure that you're not overbuilding for current demand and the demand will sustain.

Maybe I'll start, Satya and then you could add. Let me talk a little bit about maybe connecting a couple of the dots because with $400 billion of RPO, that's sort of short-dated as we talked about, our needs to continue to build out the infrastructure is very high. And that's for booked business today. That is not any new booked business we started trying to accomplish on October 1, right?

And so the way to think about that, and you saw it this quarter in particular, and as we talked about '26, the remainder, number one, we're pivoting toward -- increasingly, we talked about this short-lived assets, both GPUs and CPUs, Again, we talk about all these workloads are burning both in terms of app building. Now when that happens, short-lived assets generally are done to match sort of the duration of the contracts or the duration of your expectation of those contracts. And so I sometimes think when people think about risk, they're not realizing that most of the lifetimes of these and the lifetime of the contracts are very similar.

And so when you think about having revenue and the bookings and coming on the balance sheet, the depreciation of short-lived assets, they're actually quite matched, Mark. And as you know, we've spent the past few years not actually being short GPUs and CPUs per se, we were short the space or the power is the language we used to put them in. So we spent a lot of time building out that infrastructure. Now we're continuing to do that also using leases. Those are very long-lived assets, as we've talked about 15 to 20 years. And over that period of time, do I have confidence that we'll need to use all of that, it is very high.

And so when I think about sort of balancing those things, seeing the pivot to GPU, CPU short-lived, seeing the pivot in terms of how those are being utilized, we are -- and I said this now, we've been short now for many quarters. I thought we were going to catch up, we are not. Demand is increasing. It is not increasing in just one place. It is increasing across many places. We're seeing usage increases in products. We are seeing new products launch that are getting increasing usage, and increasing usage very quickly. When people see real value, they actually commit real usage.

And I sometimes think this is where this cycle needs to be thought through completely is that when you see these kind of demand signals and we know we're behind, we do need to spend. But we're spending with a different amount of confidence in usage patterns and in bookings, and I feel very good about that. I have said we are now likely to be short capacity to serve the most important things we need to do, which is Azure, our first-party applications. We need to invest in product R&D and we're doing end-of-life replacements in the fleet. So we're going to spend to make sure that happens. It's about modernization. It's about high quality. It's about service delivery, and it's about meeting demand.

And so I feel good about doing that, and I feel good that we've been able to do it so efficiently and with a growing book of business behind it.

Yes. The only thing I would add to what Amy captured was, if you sort of look out, there are 2 things that matter, I think, and that are critical in terms of how we think about our allocation of capital, also our R&D. One is how efficient is our planet-scale token factory, right? I mean that's at the end of the day, what you have to do. And in order to do that, you have to start with building out a very fungible fleet. It's not like we're building one data center in one region in the world that's mega scale. We are building it out across the globe for inference, for pre-training, for post-training, for RL, for data [indiscernible] or what have you. So therefore, that's the fungibility is super important.

The second thing that we're also doing is continually modernizing the fleet. It's not like we buy one version of, say, NVIDIA and load up for all the gigawatts we have. Each year, you buy, you write the Moore's Law, you continuously modernize and depreciate it. And that means you also use software to grow efficiency. I talked about, I think, 30% improvement on both serving up GPT-4.1 and 5.0, right? That's software. That's sort of -- and by the way, it's helpful on A100s, it's helpful on GB200s, and it will be helpful on GB300s. That's the beauty of having the efficiency of the fleet.

So keep improving utilization, keep improving the efficiency. So that's what you do in the token factory. The other aspect, which Amy spoke to is we have some of the best agent systems that matter in the high-value domains, right? It's in information work. That's the Copilot system. Coding, I mean, I should also say one of the things I like about Copilot is, I mean, Copilot ARPU is compared to M365 ARPUs, right? It's expansive. The same thing that happened between server and cloud like we used to always say, well, is it zero-sum, it turned out that the cloud was so much more expansive to the server market.

The same thing is happening in AI because first, you could say, hey, our ARPUs are too low when it comes to M365 or you could say we have the opportunity with AI to be much more expansive. Same thing with tools, right? I mean, tooling -- the tools business was not like a leading business, whereas coding business is going to be one of the most expansive AI systems. And so we feel very good about being in that category. Same thing with security, same thing with health. So we have -- and in consumer, one of the things is it's not just about ads, it's ads plus subscriptions that also opens up opportunity for us.

So when I look at the entirety of these high-value agent systems and when we look at the efficiency of and fungibility of our fleet, that's what gives us the confidence to invest, both the capital and the R&D talent to go after this opportunity.

The next question comes from the line of Karl Keirstead with UBS.

Okay. This one is for Amy. Amy, I certainly don't want to take you down too complex an accounting path with this question, but the investment in OpenAI that sits in other income at $4.1 billion is so large that I think the audience could -- listening in could benefit from a little bit more color about what that is. It feels like it's so much larger than you were running through other income in prior quarters that it mustn't just be your share of the OpenAI losses. So could you just describe that? And what we can expect in subsequent quarters? And whether this signals any kind of accounting change?

The Q1 number was not impacted at all by the new agreement that was put in place. Let me first say that. Secondly, that increased loss was all due to our percentage of losses in OpenAI due to the equity method. So just to be very clear. So there is not anything there that is not the increased losses from OpenAI.

The next question comes from the line of Mark Murphy with JPMorgan.

So we seem to be entering into a new era where the contractual commitments from a small number of AI natives are just incredibly large, not only in absolute terms, but sometimes relative to the size of the companies themselves. For instance, contracts worth hundreds of billions of dollars that are 20x their current revenue scale. Philosophically, how do you evaluate the ability of those companies to follow through on these commitments? And how do you think about placing guardrails on customer concentration for any single entity?

Yes. Maybe I'll start and then Amy, you can add. I mean it goes back a little bit, Mark, to what I said about building first, the asset itself such that it's most fungible. And then to recognize the strength of even sort of our portfolio, we have a third-party business, we have a first-party business, we have third-party also spread between enterprise, digital natives, I always felt that we need a balance there because it may start with digital natives. They're always going to be the early adopters. You always have the hit app of the generation. And then -- essentially then it spreads throughout. The enterprise adoption cycle is just starting and so therefore, having the -- over the arc of time, I think that third-party balance of customers will only increase.

But it's great to have the hit first-party apps in the beginning because you can build scale that then if it's a fungible and that's where the key is. You don't want to build for a digital native in -- as if you're just doing hosting for them. You want to build. That's where -- I think some of the decision-making of ours is probably getting better understood. What do we say yes to, what do we say no to. I think there was a lot of confusion, hopefully by now, anyone who switched on would figure this out. And so that's, I think, one thing we're doing on the third party.

But the 1 -- first party is probably where a lot of our leverage comes and it's not even about one hit app on our first-party even. Our portfolio of stuff which I just walked through in the earlier answer, gives us, again, the confidence that between that mix, we will be able to use our fleet to the maximum. And remember, these assets, especially the data centers and so on are long assets, right? There will be many refresh cycles for any one of these when it comes to the gear.

So I feel that once you think about all those dimensions, the concentration risk gets mitigated by being thoughtful about how you really ensure the build is for the broad customer base.

And maybe just to help with another angle that I think, Satya helped a lot is that when you think about concentration risk or delivering to any customer, you have to remember that because we're talking about this very large flexible fleet that can be used for anyone and for any purpose, 1P, 3P, and including our commercial cloud, by the way, which I should be quite clear on, it is pretty flexible in every regard, you have to remember that the CPU and GPU and the storage gear, doesn't come into play until the contracts start happening. And so you're right, some of these large contracts have delivery dates over time.

So you get a lot of lead time in being able to say, "Oh, what's the status?" And so I think we're pretty thoughtful around what's always gone in our RPO balance, and then considerate of that. There's always been that taken into account when we publish that bookings on brand, publish the RPO balance.

The next question comes from the line of Brad Zelnick with Deutsche Bank.

And I'll echo my congrats on an amazing start to the year. Amy, is there any way to quantify or frame the revenue impact of Azure being short on capacity? And while I appreciate the constraints you face are broad across the industry, is there risk of workloads going elsewhere? And how do you mitigate that?

Yes, Brad, it's a great question. It's always hard to quantify precisely what would have been the revenue impact in quarter. But I would offer a way to think about it is Azure probably does bear most of the revenue impact. Because when you think about real priorities that you have to fill first, it's obviously the increasing usage and adoption and sales we've seen of M365 Copilot and the usage of Copilot chat, which we've seen very different patterns, which we're encouraged by. It's the adoption of security features. It's the GitHub momentum.

And so when you're thinking about it, that is where and it is a priority for us to allocate resourcing there first. And so you are right to ask how do I think about that. We've worked very hard to try to mitigate it as best we can, but we have been short in Azure, and we've been clear on it. And I would say the other 2 priorities that I haven't mentioned maybe as much before is also just making sure our product teams and the AI talent that we've been able to hire into the company really over the past 1.5 years have access also to significant capacity because we're seeing it make the product better in a loop that is adding great benefit today into products people are using today for real-world work.

And so we are making that a priority to make sure our research teams have that as well as our product engineering teams. And yes, it does impact Azure directly. That is the place where you see that prioritization. But I think it's probably hard for me to give an exact number, but it is safe to say that the number could be higher.

The last question will come from the line of Kash Rangan with Goldman Sachs.

Amy, I just wanted to congratulate you, I think you said before that it is possible to accelerate Azure growth while getting efficient with margins and you've done it. Congrats on that.

I have one for you, Satya. With respect to the elephant in the room, following just being a little more direct, following up on Keith Weiss' question. There's talk that another hyperscaler came in and took away the business that was rightfully Microsoft's. I'm sure that there is a different point of view here.

I'm wondering if you could offer some perspective on your criteria to -- is it about a certain volume of business that you wish to execute on the Microsoft paper? Or is it something broader than that, that I don't think maybe people fully appreciate the terminal value that Microsoft will have on its balance sheet at the end of these contracts, which I think is probably being underestimated as you have a full stack and you've got the multiple vectors to monetize, be it databases, Foundry. And to your point that you are a platform company, not just a hyperscaler. Maybe that's what it is all about, or maybe there's another story about you letting the other hyperscaler company coming from nowhere and claiming a big piece of that 4- to 5-year puzzle. And congratulations.

Well, thanks, Kash. I mean for us, again, just always goes back to, I think, the core principle, which is build a fleet that is fungible across the planet and works for third-party and first-party and research. So that's essentially what we have done.

And so when some demand comes in shape, that don't fit that goal, where it's too concentrated, not just by customer, by location, by type of skewing, right? I think Amy mentioned some very key things. When you think about the margin profile of a hyperscaler, you've got to remember this, the AI accelerator piece, but there's compute, there's storage. And so if all of the demand just comes for just one [ meter ] that's really not a long-term business we want to be in. That's even from a third party. We have to balance it with all of our first-party stuff because that's after all a different margin stack for us. And then we have to fund our own R&D and model capability because in the long run, that's what's going to differentiate us.

And so I look at all of those. We sort of use all of that to make sure we are saying yes to all the demand that we want, we say no to some of the demand that may be something that we could serve, but it's not in our long-term interest. And so that's sort of the decision-making we have done, and we feel very, very good about the decisions. In some sense, I feel even each time we say no to, the day after, I feel better.

And just Kash, I think this is our last call with you. And I just want to say thanks and congratulations. It's been a privilege to work with you and best of luck.

Let me add to that, Best of luck, Kash.

Thanks, Kash. That wraps up the Q&A portion of today's earnings call. Thank you for joining us today, and we look forward to speaking with all of you soon.

Thank you all.

Thank you. This concludes today's conference. You may disconnect your lines at this time, and thank you for your participation.

As the music dies down, the AI activity and IQ from our next guest is going to heat up. Did I set you up too much? I think I've heard good things about...

High expectations. High expectations. Yes.

Well, our next guest is Jared Spataro. I will have Jared walk us through his background. We would love to find out what you've been doing at Microsoft for the short span of 20 years. You look very young, and I find it very hard to believe that you've been at Microsoft 20 years. But tell us about your background and career.

Sure. So I was educated as a computer scientist and started there. It's always been my interest, and energy has been there. When I came to Microsoft, it wasn't at the beginning of my career, so I didn't start there. I worked at smaller firms prior to that. I was in product, essentially product management, and I went to Office. So I worked on the Office set of products.

Over the course of the years, I started to migrate more towards the business side of what we do. So we put in product marketing as the business management side, and we typically will pair that up with an engineering lead. So now I'm responsible for a portfolio that we just call AI business solutions. But over the years, I ran everything from Office Commercial to Office Consumer to Windows to Office 365 to Microsoft 365. The work during the pandemic on Teams was all work that I did. And then just when I thought I was getting bored, this whole thing started to happen with AI, OpenAI and Copilot. So that's what I'm up to now.

Got it. So although you're a technical person by education, your title is Chief Marketing Officer. Is the bar so high at Microsoft to be in marketing?

Well, these days, I think it helps a lot to deeply understand the tech for sure. Like you're -- I don't know. I love Microsoft because it's a place where we expect our business people and the CMO, if you will, to not only understand the tech but to help shape it. And that's the part of my job I like the most, is trying to chart the future where things are going.

Great. On that topic, how do you envision AI evolving over time? So Microsoft has got a unique vantage point being super, super early. So explain to us how you envision the AI tech stack evolving over time, your vantage point and Microsoft's role in this evolution.

Yes. Let me talk tech stack, but I'll be brief. I don't want to go too deep, but you certainly can ask more questions. We start at the hardware level, obviously, all the way at the chip level, data center level. There's been a lot of innovation not just in chips but in data centers over the last couple of years.

On top of that, we actually start next with the data layer. There's some important work that we've done, particularly with a set of investments that have gelled into what we call Fabric now. Then we have something called foundry, where we think about the model layer. This is where the LLM sits, and that's been important for us. Next up, we move to the dev layer. Here, we split things between what we think of as pro code and low code, so those things that are designed for makers versus professional developers. And then finally, we round things out a little bit differently than most because Copilot for us is kind of that front end, if you will. We think of it as a platform that we're really trying to draw users into not with a daily habit but in fact, with an hourly habit in the knowledge worker space. So that's how things compose.

We spent a lot of work, if I were to take a step back though, trying to project out 3 to 5 years and say, well, what's the firm of the future look like. We've decided to call that idea of the frontier firm just using that frontier label from models. And we're pretty excited about what that looks like. We can describe it today as a firm that we would say is human led but agent operated. And we think that it behaves very differently. We think that humans play an important role, but the patterns of work between humans and agents are going to be very transformative. So that's kind of where we are.

We see Microsoft's role in there is helping businesses, organizations get from today to that frontier firm. That's how we would frame up how I have conversations with CEOs and their teams, technical teams and business teams.

Got it. We all watched the launch of GPT-5. I'm sure you did. What is your reaction to GPT-5 launch? What do you make of the technological enhancements? And I have a follow-up question as it relates to Microsoft 365.

Yes, this one has been -- this has been interesting for me because for me, it separated out a little bit people who understand what's happening versus those who are kind of superficially just tracking the technology. Let me explain what I mean.

GPT-5 was a systems launch. A lot of people focused on, hey, wait a second, we don't see -- we were looking for nonincremental model differences. And I understand that. I understand the expectation. But if you get to the heart of what was going on, the biggest thing that was introduced with GPT-5 was at the orchestration layer, what they call the router, move from being a deterministic hard-coded thing to being an LLM model that had been posttrained to orchestrate other models and tool usage.

And so today, with GPT-5, when you send in a prompt, it's deciding do I route this to a reasoning model. Do I route this to a cheaper model? Going forward, that system, I can't explain how important that system is going to be for actually realizing the value of agents and the value of what's happening. So I understand the chatter about I was looking for more in terms of the model. This was a really important systems launch, and it exposes, in their case, for that ChatGPT, it exposes the broad swath of free users for the very first time to reasoning models at the right time in an economical type of way. So I think we may talk a little bit more about that, but that systems-based approach, I think, is really, really important because it's the beginning of a system that is going to prove to be very influential.

So you just got me thinking, so the beginning of a system that's going to prove to be very influential. Where do you go forward with this? How do you make this more useful in terms of Microsoft approach to this technology?

Man, it's a great question. We think of -- here's what the state of the art would tell us right now. There is a question about can you create essentially a super app that is so smart model-wise that it can do everything you could ever imagine doing with superhuman capability. And it turns out that the answer, we would think at Microsoft and I think many of the model producers feel, is no. In fact, it's better to go and train agents that are domain- or area-specific that prove to be superhuman and then essentially to have a system that pulls on the right agents at the right time to answer the problem.

So we look at this in commercial terms and say, man, we were just given a gift as an industry with essentially an orchestrator, conductor of the symphony that with time is going to be able to register up the right agents and those agents will have real domain expertise and then call on those agents in an orchestrated way to get incredibly sophisticated jobs, not just tasks, but jobs done. That for us is the future of this frontier firm.

We expect that companies will produce financial analyst agents. They're going to produce expert scientist agents. They're going to produce actuarial agents. And this router is going to call on them to do the right things in business process. So we look at this moment. That's one of the reasons we sim-shipped GPT-5. We worked so hard to sim-ship that in Copilot, is it's a real watershed moment for us.

Yes. So the bleed over to M365 Copilot, how does this manifest going forward, the GPT-5 improvements in M365 Copilot?

Well, same day, we replaced the back end of Copilot with GPT-5. You -- currently, if you use Copilot, you have to select a button. That was just us making sure putting running water through the pipes, but in the next couple of weeks, you'll see that turn to the default. And what it means for us is it's the realization of a strategy that we simply would call Copilot plus agents.

We think of it this way. Copilot is to agents like the iPhone is to apps or in other words, just like the iPhone has become a platform or a window into the world of apps over the last decade, we believe that, in a commercial sense, Copilot will become a window or a conduit into the world of agents. And what we envision happening is Copilot will help you at the right time select the right agents to solve the business problem that's in front of you. That's what our customers are asking for. They're saying, "Hey, I love the power I'm seeing, but I need it to be specific to my business. I need it to be specific to my industry." and Copilot is architected to do exactly that.

Got it. Any examples of that? So you talked about the financial analyst. Any other examples besides putting me out of a job?

Sure. I can give you a bunch of different things. Across the industries right now, customers are working with us, either pro code or with something we call Copilot Studio to create purpose-built agents. These agents typically are being trained on like a reasoning model, and then you can take in their own content and train it.

So I'll take pharmaceuticals as an example, and you can take something like the clinical trials or you could take a part of the scientific process. There are a bunch of different jobs that happen along the value chain of clinical trials, and what our customers are starting to do is to build agents to do those jobs. Someone, for instance, is as simple as a technical writer. Another person is -- prepares the submission to the FDA. Other people summarize, for instance, the actual trial data.

I mean there's a bunch of things that happen. You can stack those agents up, and you can essentially design an end-to-end process where Copilot can do the orchestration for you of, hey, I want to run this and pick up where the data is today, and I want to start to run it down to the process of getting to an FDA submission.

It's not done by Copilot itself. It's done by Copilot orchestrating these specialized agents to do their jobs. And that really moves us into this realm of not just agents but digital workers, digital employees.

I'm going to throw out a bunch of seemingly unrelated terms and ask you to see if you can explain where this concept kind of fits in this, RAG, context -- broader context windows, reinforcement learning, test time compute. In that realm of seemingly unconnected things that I just blabbered out, where would this enhancement to GPT-5 fit in? Is it a broader context window or none of the above, and it's like that's mad rambling, Kash, and it does not make any sense?

No, it's good rambling. I have to put them together though for a second. It was RAG and...

RAG, broader context window, reinforcement learning, so those are all things that are supposed to enhance the core model in some way, add context to it, provide information to it, hold it more in memory. Whereas what you're talking about is getting deeper into domains. Does that happen because of any of these things? Or does the model itself come with the deeper domain and you just have to call upon that, okay, I want to pull upon the financial analyst domain, boom, it goes and pulls [ that ]?

Yes. Okay. I think I can construct in a way, if you're following the conversation out there. Think of what we're describing as a way to create a system of agents that has an orchestrating agent in Copilot, essentially directing traffic or orchestrating a team of specialized agents who do work.

Now take, for instance, the things that we just threw out there. All those things that Kash has talked about are ingredients to make typically those specialized agents more effective. So RAG is a way to, for instance, add very specialized context to an LLM so that it can reason over data. The context window creates a bigger kind of space of context for people to be able to do that.

So the thing we're talking about here is a system of orchestration. A lot of what we've heard from the industry so far has been almost like incremental improvements on an agent per agent basis. And this is about how do you get nonincremental improvements because you have a set of agents that are working together in a structured way.

Got it. Not step function, but orders of magnitude better.

Yes, that's exactly right. Yes.

That was what was echoed by our previous panelists from Sequoia Capital that he said that people misinterpreted the GPT-5 as an incremental advance over GPT-4.5, and he made it look like it's a lot more than that, which seems to be very consistent with how you characterized it.

I've watched Build, and I was fascinated. I was not prepared to be riveted to every second of what happened at Build, and I was riveted. In particular, the MCP, support for MCP protocol, the agent-to-agent protocol and what seemed like you were building a middleware layer, just my mind flashed back to BEA Systems. That may not mean anything to you, but maybe some of us here in the audience might actually recollect BEA Systems. They produced middleware for SaaS web-based applications. Of course, the company went nowhere, but they were -- at the beginning, you could see the future if you envision, right? It felt like you're building middleware for agentic applications. So talk to us, if you don't mind, the implications of these frameworks A2A, MCP as it pertains to AI apps ecosystem development? How important is this?

Yes, it's a great question. To answer the question, I'll back up a little bit and just say there is a huge delta, a gap between the capabilities, the raw capabilities we see of the technology, particularly the models right now and the value that commercial customers are realizing. I think all of us understand that. Otherwise, there would be different things happening in the market.

The thing that we see that is the gap to be filled essentially is how do you practically take these very powerful workhorses, these engines and put them to work in the very complex plumbing that is today's enterprise. And as you're talking about MCP and A2A and these protocols that allow agents to talk to one another, that's exactly what we're doing.

We see our role at Microsoft as taking the best innovations from all over the industry from all sorts of different providers of those innovations and providing a platform for our customers to build solutions that actually just kick out business results. And it could be whatever the industry is, if it's consulting or if it's pharmaceuticals or whatever happens to be, really produces results. But to do that, you actually have to start to fill the gaps in there. And so for instance, having agent-to-agent communication even for what we were just talking about with the orchestration, it's key. It's foundational. It's really important. Might be -- might not be the most exciting thing, but we're trying to really build that up, build that stack at every layer so that there's connections there and then it's easier and easier to program.

That -- when Satya Nadella talks about foundry, the foundry layer has all these protocols being supported. And then if these protocols are supported, then you would need to pull into a data layer that would have all the semantics, the context, et cetera, to make the agents actually do the work, right?

That's right. And that's -- for us, that's where, as I rattled off, Fabric is that data layer. You don't have to move all your data. Fabric can both do in place as well as data that you move over. Foundry, whatever models you want to with, for instance, communication between them, you've got exactly right. Yes.

Got it. How dependent is the Microsoft Copilot stack on OpenAI's IP? And are you diversifying your tech stack beyond OpenAI?

This must be the question of the hour, I would imagine, right now. OpenAI is our first...

There's something about this MAI model that Microsoft is doing.

Yes. OpenAI is our first and best partner. No doubt about it. We have a very unique relationship, but it's a very important relationship for both companies. And the Microsoft 365 Copilot runs on the OpenAI models. GPT-5 sim-ship was another vote of confidence. We think that they've got the best technology out there. So it's really important to understand that we feel like it's a great partnership.

That said, you just talked about the tech stack, and you did a great job of hitting at the layer of foundry. We have been now for some time, for some number of quarters and particularly earlier this year, very open about the fact that we want to give enterprises access to just the best technology out there. So that set of announcements at Build, what most people didn't understand is using foundry, you could choose any model, make an agent and essentially mount it in M365 Copilot already.

That means you could choose a Llama as your model. You could choose whatever you wanted to out there from any other provider as a model and put it in there today. So that's been an important part of the way that we are pursuing the business. But OpenAI, first and best partner, we have a very unique and very important relationship.

So the role of MAI is just one other model. It's a Microsoft first-party model. Yes. Where is that, I mean, going? What are the plans for MAI in the context of the foundry layer?

Yes, we would say it this way. We've always said for a long time, Satya was one of the first people to say this, that we believe that models will become commoditized over time. There are many ways to hear that statement, but one way to understand the statement is there is some threshold that when you pass, you're already so valuable that what you're putting on top of that becomes essentially diminishing returns for many tasks that you need to do. So us developing our own models is -- it's very consistent with that idea that we believe that these, at some point, are going to become commoditized and that you're going to say, hey, there's diminishing returns. We don't need something more powerful for the general purpose.

So frontier models will always have their place, pushing science and pushing the boundaries. And yes, we've said that we're going to develop our own models. We've said that we are not trying to develop frontier models, which is interesting. And so that -- we may change that perspective. You shouldn't take that as anything more than kind of how we've talked about what the MAI models are right now. We've said that it's not meant to compete, for instance, against GPT-5. But the model space continues to change, and we hold that the -- it hasn't reached a place where we feel like it's just slowed down in its overall progress.

We've heard so much about the AI, the threat, potential threat of AI disrupting seat count or put pressure on seats due to layoffs or maybe intentions to hire at a slower rate because of AI's ability to write code, do design and who knows what else it's going to do. Where do you stand on this debate?

We're trying to take both positions, I'll say. So let me talk about both positions. Here's what we'd say. Oftentimes, when people come in and say, "Oh, my gosh, the world is going to change," they think it's going to change overnight or at least that's kind of where the mind jumps to, some amazing technology that's going to change overnight. There's no doubt that if we -- if our hypothesis is true that there will be human-led, agent-operated enterprises, that agents will become more important -- more and more important in doing work and that, that will have an impact on the work that humans do, for sure.

But how fast that will happen, what the humans will do, et cetera, we can't predict the future with perfect fidelity. And so we would say this. We have a business model today that has a per-user licensing basis to it with M365. We like that. We continue to grow it. We grow seats. We grow ARPU. At the same time, we increasingly have an agent-based business model where we are charging in 2 different ways per agent, and we charge per consumption. Our customers can choose. So we kind of have our foot in the 2 camps right now, and we think that's the right place for us to be as a business because we're just going to watch how the dynamic plays out over time.

What are the productivity benefits that you're seeing with your customers on account of adopting Copilot at this stage? It's been out for a couple of years now.

Correct, yes. Correct. They fall largely into 3 categories. Category 1, we would call personal productivity and that's about making someone who already does the job mostly do that job more efficiently. And here, we've -- for the most part, we feel great about what we can do. We think, in many tasks, we can improve efficiency by 20% to 30%. And if you do kind of like control and treatment type group experiments, you can show that. The most difficult thing about that is it's tough to drive ROI on saying Kash is 30% more productive like unless he's a salesperson and carries a quota, quite frankly, because a lot of knowledge work doesn't translate directly into top line, bottom line. It's a team that has to work. So we continue to do that work. We feel good about it, but it is hard to make the ROI argument for it.

Most of what's happening in terms of actual OpEx savings is happening in process-based applications. So by that, I simply mean people are picking a process out there that runs, that they measure, that they have KPIs with. And they're inserting AI in 2 ways, either incrementally improving that, which is important to some sort of continuous improvement type of way or, in some cases, they're entirely redesigning the process and saying, oh, we think we can do this differently.

So to make that very real for you, a lot of people have started, and we've heard that a lot but in customer support and customer service. So incremental improvements, for instance, are throughput by customer human customer service agent. We've done some work at Microsoft, where we've improved throughput of our agents by about 12%. That's real money saved for sure.

But there have been other places in customer support, where we just entirely redesigned the system and we tried to do what we call deflection upfront. Never have it get to a human agent. Can we answer your question essentially with AI upfront? That's a different process, and we've been able to say about half of the savings we've realized have come from that deflection. So just real nuts and bolts. It may not be the most exciting thing to talk about, but at Microsoft, we're saving a lot of money, and we see our customers doing it as well.

Well, I use Copilot to draft e-mails, and I make very little corrections to it. And I also ask it to summarize my calendar for the day and what are the priorities. I even ask it to prioritize the e-mails that I need to respond to. And sometimes it'd look at an e-mail and say, oh, #1 important e-mail would be like some client asking me to comment on a wildly speculative thing that's happening that it thinks it's very important because it's worded in such -- you have to please get back to me on this, right?

But on that, I think it's been just generally a tool that has been growing. And I start to use Word, not that I use PowerPoint, but I get into it. Copilot starts to show up, and, "Can I summarize this for you?" It's turned -- it started to show proactivity, just like ChatGPT, say, "Would you like me to do more research on this?" It's starting to show up a little bit more and more and more.

Yes. And we're excited about what you'll see from us this fall because we finally feel like with GPT-5, we're starting to get away to be pretty sophisticated about some of the things you're talking about. I'll give another couple of examples quickly or maybe one. A lot of people don't think about it, but we often work outside of our native tongue. You may have to do something in Italian. People have to do things in English that isn't native. We see lots of people using Copilot actually to help them communicate in something that is not their native tongue incredibly eloquently as an example. It's a small thing, but it really matters. It's a great example of something that's not currently being measured but makes a big difference at the personal level.

I think some people would say that Wall Street uses too much jargon in cRPO, RPO, KPI. I mean, translate all that stuff. Lay it for me in English, right? We actually have a jargon dictionary in the Goldman Sachs research. You had to [ head to LinkedIn ]. It's like a massive one, a pretty long one because every industry analyst has their own like jargon list. 1 day, that goes away.

As of the Q4 earnings, which were phenomenal, Copilots are now more than 100 million MAUs across commercial and consumer. Where are we in this adoption cycle, the first inning, second inning, third?

We're very early. Yes. We would say we're very early. Our -- in the commercial side, which I'm responsible for, the motion is land and expand. The last data point that we released was a couple of quarters ago, and we talked about the fact that 70% of the Fortune 500 are using Copilot in a pretty extensive way. And those are lands, and we're expanding from there. This past quarter, when we reported, it was our best quarter ever, both in terms of seat adds for Copilot and the number of customers coming back to add seats in a big, significant way. So we feel like we're early.

One of the things I said in an earlier meeting today that I'll just repeat is the biggest difference that I would flag for the group, Kash, is that, over the last 6 months, I've seen a marked change in most enterprises, whereas 6 months ago, they were like, well, maybe we'll do something where we provide kind of some sort of assistant at work for everyone. Most companies at this point have decided they're going to do that and that they're just trying to decide who will I go with or what will my estate look like. And that's a big change because once you start to lay down that baseline layer, I think it improves the literacy, helps people to find other ways to use the technology.

Got it. How is Copilot usage tracked relative to your expectations? How are those usage patterns changing?

Well, let me just say my expectation -- we'll talk about this group's expectations for a moment. My expectations would be, when you look at this, it still is our fastest growing M365 portfolio product that we've ever had. And that's saying something because M3 and ME5, like those are big important products that continue to really grow. So I really need to stress if you want to get a sense for it, like it's growing faster than either 1 of those 2.

In terms of usage intensity, we are really encouraged. When people start to use it, they do what you just described. They start to use it, and then we see the intensity increase in fact, user by user as a general trend. So we like what we're seeing there. The only workload that we feel like is more intense like during a ramp-up period ended up being Teams, and that's because we have the pandemic, and it was -- there is a single world event. So it's got a lot of intensity.

That said, if I were to sit down with each one of you, I think mostly what I hear from financial analysts is what's taken you guys so long. You have 430 million seats out there. Like what's the seat count? What's the penetration? What does this look like? And it's just early innings. It takes a while. This is a business where we've been trying to say we feel like we've got a great position, but we've got to land. We've got to expand. We've got to help people change the way they work. There's some transformation required.

I have to say another Copilot usage, which really was absolutely productive for me, was -- there was a point in time, March or April, I forget. There was this thing, oh, Microsoft is going to cut CapEx, their whole hyperscalers or blah, blah, blah. I had the benefit of your most amazing IR team on the road, and I had conviction. And I -- we wrote up a report, and I wrote up in my e-mails my thoughts on CapEx. And one client -- and there are multiple e-mails sent to multiple clients on this topic, covering multiple aspects. And 1 client called me like 2 minutes before the company was reporting -- is going to be reporting earnings. And he said I want your thoughts on Microsoft CapEx. I said I'll call you right back.

I just went to Copilot and said summarize everything that I've said about Microsoft CapEx in 5 bullet points. Boom, I sent it to him, and he was ecstatic. He was like I really appreciate you getting back to me so quick. Thank you, Copilot.

I love it. Copilot win.

I'll not mention the client's name. I don't think he's here. He may be here. I'm not...

I will say that's one of the big differentiators for us, is access to your work data cannot be -- I don't think it can be overplayed. And it's really important to understand...

I checked the e-mail, though, before I sent it.

Good. That's the right thing to do. AI can make mistakes. That work data is very valuable.

Yes, yes. What is -- who's Copilot's competition? When you try to sell this, who do you run into? Is it like ChatGPT modified by the customer to pose like Copilot or...

Yes, there are 2 classes of competitors that we see. We see other chatbots, typically by the model makers out there. And then interestingly enough, we see competition from roll your own. IT is really excited about creating the ACME Corporation chatbot based on GPT-5, like so excited about doing this.

So that's an interesting dynamic that I'm not sure I would have predicted. Now I feel like I understand what's going on there. ITs, they're builders. They like to solve problems and build, so we see both those things.

For the most part, we really believe that those folks who are rolling their own are not going to be able to keep up. They won't keep up with the iterations of the model. They won't keep up with the innovations and user experience, et cetera. But right now, it is a portion of what we see as also challenging those of us who are making our own kind of assistants at work.

I mean there's this tendency, the beginning point of any new tech cycle that when you have the building blocks, you're excited to build your own thing. And you feel like you're a genius developer, and everybody is a genius developer, creative, et cetera. When things kind of settle down, you don't -- that's not the core competence, and you want to rely on a provider like Microsoft that has watched hundreds of customers and has assimilated the problems and makes the product better. And what if the person that developed this just leaves? You're stuck. Who do you call, right? Not so simple.

Yes. That's right.

The decision on part of Microsoft to price M365 Copilot at $30 a month, does this -- how do you expect this to change over time?

We feel good about the price point. I'll say without going into great detail, we've been able to hold that price point quite nicely. We have lots of, you can imagine, discounting pressures. We do big deals and things like that, but we've held the price point. So that's good. The product is growing into the price point, frankly. That's what happens. You start with technology. It gets better over time. We really feel like, for instance, our researcher and analyst agents that are a part of the price point are examples of us adding additional value and capabilities into it.

Our initial pricing of that was based on some research that we did really early out of the gates, looking at what the value would be and trying to not just have it all get squeezed out by customer expectations that would be included into the suite. It was our feel that with 430-plus million users as a base, we were looking for a way to monetize the great gains that we give them. So that's kind of how we started.

Got it. Okay. E5 upgrades have been a big driver of ARPU increases. Could you quantify for us or maybe give us a range what percentage of the base is on E5 today? And how large is the opportunity that's left ahead of you?

Yes. We occasionally publish numbers here. Our last published number actually is not very new, so it's about 3 years old. And 3 years ago, we talked about E5 being 12% of the base. So that's the official number that we gave out. We don't have an updated number that we've shared. But suffice it to say, there's still a lot of upside with E5. So again, you can compare the 12% and think of our latest number being about -- a little over 430 million, the base.

The denominator. Yes. That's right. And Copilot has to be a catalyst for the...

Absolutely. Well, I should also say what we're really excited is there's real synergy. First, E5 and Copilot are the 2 ARPU drivers for the business, but there's great synergy. As you start to think seriously about moving to Copilot, one of the big questions you have is how you're managing your data estate. And that plugs into Purview, which is a component of E5. And so security has always been the driver of E5, but we're increasingly seeing the data labeling, sensitivity, those types of things, compliance really driving with Purview. So we've got a nice connection there between those 2 drivers.

Got it. We've got about 2 minutes left. Are we okay to get a question from the client? Or should I just go on? Anybody. We generally don't get any questions because it's such a large room and by the time the mics get to the client -- is that the case? Or who's going to be the bold one? Yes. All right.

Okay. So I'm going to make the question that people would like to hear. Your partner, OpenAI, just signed a gigantic multiyear contract with another cloud provider. Why not Azure?

I'm really not in the best position to answer that one, and I'm not joking. I don't run Azure, and I don't have much to say to be honest with you.

Yes. That's...

It's good question.

You're not the right person for that. It's a good question. Yes. You should contact Microsoft IR for that. Anybody else? Yes. This is a question that has to be addressed to Jared given the scope of his work. So yes.

Talk about Microsoft [indiscernible] enterprise [indiscernible] software application [indiscernible] in the broader ecosystem of the enterprise.

Yes, beautiful. Well, we wouldn't say that we can perfectly predict the future, but what we see happening right now is that the patterns associated with knowledge work are all being redone. Like they're being rewired at this moment. We definitely have an ambition just like we did with Office of being the front end of those patterns. We think Office was very unique in that it brought together some things that people didn't think went together.

At the time when we brought together the Office Suite, people didn't think everybody needed Excel or everybody needed PowerPoint or everybody needed Word. Those things were kind of apportioned out depending on your role. So as we look at building that front end, we're trying to find the same analogous set of patterns that we can bring to the knowledge worker of the AI era. I'll just start there. That's the ambition.

Now what happens to this question of SaaS providers? Are they all dead? Where did they go? We would simply say, gosh, those businesses are valuable. They have valuable data in them. If I choose any one of the SaaS providers, they typically have really valuable logic, business logic inside them as well. That said, people are going to increasingly, we believe, not want to go to those systems directly to do work, but they're going to want agents to be able to do that work for them.

So I don't know exactly how it plays out. We don't think that those companies go away automatically, but we think the workflows, the data flows, even the architecture is going to change in very significant ways. And I would say it this way. We're trying to really be in position to ride that wave, and I think every company needs to think about what position they should be in.

With that, let's give a round of applause for Jared. Thank you so much. Great discussion.

Great to be here.

Okay. I think we're live. Awesome. Welcome back, everybody. Once again, I'm Brad Zelnick with the Software Research team here at Deutsche Bank. For this session, we are very pleased to have none other than Microsoft specifically, Vasu Jakkal, do I have that right?

Right.

I always take a little bit of risk in pronouncing a last name, but who is Corporate Vice President of Microsoft Security, which for those that don't realize is the most significant security business of all security businesses worldwide by a significant factor. But format of this presentation is going to be a fireside chat. I've got a bunch of prepared questions that I'm hoping we can get through, and we're going to get a lot smarter on the topic of cybersecurity and why it's so important to Microsoft. So with that, thank you so much for joining us today.

That's a pleasure, Brad, and it's nice to be in Dana Point with you.

Isn't it lovely?

It's beautiful. I might even take up golf now, but this...

[indiscernible] this would be a perfect time and a perfect place. Well, again, thanks so much for being here. Maybe for those that don't know you and are less familiar with Microsoft Security business, can you just take a minute to explain your role at Microsoft and what your mandate is?

Yes, absolutely. So Thank you, everyone, for joining. It's great to be here with you all. I've been at Microsoft now for 5 years, and I'm responsible for our Security business. So I joined really to build a business and I oversee our security portfolio end-to-end globally that involves defining the strategy, working with Charlie, our engineering leader on products and then, of course, all the go-to-market, including business models. And this portfolio is pretty broad. We have 6 product families as part of that, and we really protect end-to-end. So starting with threat detection and security operations, defending against all the attacks coming at organizations. That's our Defender and Sentinel product line. We have Identity. We started with Identity Management, but we have Protection and Governance, that's Entra. And that picture, I think we'll have a picture for you that shows the portfolio. We also have device management and that's Intune. And then finally, given, of course, AI data security has reached a critical point. And we have Purview, which is our data security Protection and Governance family. So those are all of the families within the Microsoft Security portfolio. We also have our generative AI Security Solutions called Security Copilot, which integrates with each of our products and also it can be used stand-alone and that constitutes the Microsoft security platform.

Thank you for that. And I think we do -- there it is.

That's it.

Excellent. A picture is worth a 1,000 words, I think they say. Thank you for that overview. Super helpful. And maybe just at a very high level, with nearly 1.5 million Security customers. That's a massive number. Microsoft clearly has a unique perspective into broader trends and what's going on in the world. What are you seeing in the current environment?

Yes, it's a great question, Brad. So we're seeing 3 challenges, which are -- which every organization is facing right now. The first 1 is the threat landscape. It's at unprecedented levels right now, the speed, the scale and the sophistication. And just to give you an idea, last year, we were seeing 4,000 password attacks per second because Identity continues to be a battleground of security. This year, we're seeing 7,000 password attacks per second. So that's 600 million attacks every single day. So that -- just the scale is a lot. The speed is also -- has also increased. On average, it takes attackers 72 minutes or less to infiltrate an organization. So from when a user clicks to a phishing link to when the actor gets access to your data, sometimes your [ full ] inbox. That's not a whole lot of minutes for a Defender to protect an organization. And then we're seeing the number of attackers has also increased. The cyber crime is a $9.2 trillion anti-economy every year, that's increasing. And just from the number of attackers that we are tracking at Microsoft, last year, we were tracking 300 unique nation-state and financial crime actors. This year, we are tracking 1,500. So that's a 5x increase. So that's challenge #1, is unprecedented levels of the threat landscape. Challenge #2 is data. Data risk and insider risks are increasing. 20% of data breaches are caused by insiders, whether it's intentional or unintentional, doesn't matter, but that's kind of the stat. 80% plus leaders are concerned about data, especially as AI becoming mainstream. And then the third challenge is complexity, fragmentation continues in the security industry. This industry was built by a lot of bolt-on tools. On average, organizations have 40-plus tools, and they have to stitch it together, especially when the security talent doesn't exist. There's more than 4 million jobs, I think 4.7 million was the latest stat, which are unfulfilled in security right now. But they don't have the talent to fit that, and then you have a very complex regulatory environment. This year we, I think, there are 100-plus AI-related regulations. Every day, there are 250-plus regulatory updates. So that's a lot for organizations to deal with. The challenge #1, threat landscape; two, data risk; three complexity. And what we're seeing in terms of trends and solutions now is GenAI. So both using AI for security, like how do you turbocharge defense. That is a super power that we have to leverage. And we're also seeing security for AI. You have to secure all of this AI and then the last 1 is consolidation, simplification and end-to-end protection. So that's what we're seeing aligned to these challenges.

IIt's a lot to keep up with, I don't know when you sleep. By the way, does everybody in the room hear Vasu, okay? I just want to make sure. Okay, cool.

Great. Me doing my own AV check. Like I said, a lot to keep up with, and the interesting part about cyber, it seems it's the only adversarial aspect of IT. It's been a perennial game of cat and mouse. And the industry feels more competitive than ever with multiple platforms, all attempting to consolidate across various segments. Can you just help us understand Microsoft -- how Microsoft differentiates itself amidst what is such a noisy landscape out there?

Yes. It's extremely noisy, and I love what you said, it's a cat and mouse game. I feel like for those who like hiking, I feel we are like hiking up Kilimanjaro and someone's actively throwing boulders at us. That's what it feels like in security any single day. But in terms of -- and this is the main reasons Microsoft is in security because we are a software and a platform player, and we are security players. So we have strength across both. And our biggest strength is that of the platform. But the 3 things that make us unique. The first 1 is signals. You cannot defend what you cannot see. Every single day Microsoft processes 84 trillion signals. And just to give you an idea of that magnitude and the growth, when I joined Microsoft 5 years back, we were tracking 8 trillion signals. So that's a 10x investment and increase just in 5 years. These signals help us understand who the attackers are, what are they up to? How are they attacking organizations? And you take that and you marry that with our human threat intelligence of 1,500 threat actors, we do have the largest and deepest threat intelligence in the industry. So that's number one, which is our differentiator. Number two, we talked about the complexity, the fragmentation. Attacks come from everywhere. They're not going to just start with your end point or your identities. We have to like monitor it all. Microsoft has the most comprehensive portfolio. We've been believers in this end-to-end. So you can't just protect your endpoint or identities, you have to look all around. We integrate 50-plus categories today and we bring them to life in that portfolio. So you can have a consolidated solution from Microsoft. The third thing is we make sure that we have best-of-breed solutions in the areas that we are participating. So whether it is Identity, which is where our roots are, its device management, it's data security, it's cloud security, it's endpoint, it's SIEM, SecOps. We have best-of-breed in 19 categories across the industry, the only leader which is best-of-breed in the largest number of categories. And then to stop all of this is GenAI. We were at the leading edge of GenAI in 2023 when ChatGPT and Copilot became mainstream. So we have this deep understanding of what is needed for AI and the AI stack and leveraging that really to understand, well, how do you secure this AI, both using AI for defense so that's Copilot and we were the first to market with a GenAI solution. In fact, our security Copilot is used by more than 1,000 customers and there are some statistics which are showing how they are making a difference, like 30% reduction in mean time to response. And we are securing the GenAI solutions across. So I think those 4 things signals, making sure that we have end-to-end protection with best-of-breed that we have GenAI and that we are continuously building a platform, integrating the ecosystem is what sets us apart.

No doubt, puts Microsoft in a very differentiated position. I want to double-click on AI and I feel like -- we've almost made it 10 minutes and we didn't really spend too much time focusing on, it's [indiscernible].

You think of AI, right?

You've heard o this, right? So yes, it's a multidimensional topic. So 3 related questions. You've touched on this a bit already, but how have you seen the threat landscape change with AI specifically? I don't know if you can further quantify or give us a sense of what the impact has been. How should we think about Microsoft's response to better leverage AI security Copilot, et cetera, [ learn ] a little bit more about that and how we're going to protect AI itself?

Yes. I know it feels it's like water now. It's all around us and we're spinning in it. So from an AI standpoint, the threat actors are really smart. They are going to use every tool which is available to them. And so the types of attacks that we can expect to see and we're seeing is every single dimension that you think of, whether it is identity attacks of phishing attacks, think of them just getting faster and the scale increasing. In phishing, we're seeing social engineering. I'm sure you've seen a lot of the deepfakes on how AI can be used, very convincing. And they have more context now. They can do [ Recon ] very easily. We published a report almost a year back with OpenAI on some of the nation-state related actors and what they're using AI for, and we saw that they're doing a lot of [ Recon ] with it. But using that context, getting smarter, really mimicking humans more with phishing and providing that, they're going to see that. Identity, we talked about identity being the battleground of security, with AI now you can do faster password cracking. I mean how many of us still have passwords? We still live in a password world. So that's going to be challenging. Everything from command and control to make do lateral movements and how they're using AI to just get better intelligence on your organizations and what we're going to see from threat actors. But more than that, I think one of the Other aspects of AI is it introduces new surface areas. So in addition to what we consider existing surface areas, your devices and identities and network, think about prompts, that's a new attack area. Think about LLM models, think about AI-related data. So we are going to start seeing them also leverage those attack surfaces and try to get into organizations through that. So it's using our existing attack surfaces and they're really trying to leverage AI to get smarter and faster and inside the [indiscernible] and leverage the new surface areas to use that as a leverage for them. Now in terms of Brad, you asked, how are we going to protect against that? So you have to think about protecting the entire AI stack. And so foundational security, whether it's modeled, it's the AI stack itself. It's what we call built-in content safety, all of those measures, we have to have that. And then you have to have security measures all around, so you're monitoring and your governing AI while it is being used in organization. So that's how we are using AI and of course, we have to use AI for security because you can't keep up with these attacks by just what we were doing earlier. We have to start using GenAI.

We need AI to fight AI.

You need AI to fight AI, beautifully said.

It makes a lot of sense. Microsoft is obviously continually innovating, once stat that stood out to us, is that 5 of 11 recently released security Copilot agents were developed by partners such as OneTrust and Tanium with 2 names that I remember. But can you tell us why Microsoft's collaborative approach to the ecosystem is so important?

Yes. We've always said that security is a team sport. And if you truly look at what we're all up against is these very smart threat actors who have unlimited resources in many ways. I mean cyber crime is a gig economy itself. So we have to collaborate as an industry, and Microsoft is a big believer that we all have to work together and figure out how to stitch together our solutions so that we can defend end-to-end. We built a platform that's Sentinel, and we have 350 connectors in it. And as agents get developed and they're going to use these platforms, they're going to use the data, we need to make sure that we are enabling everyone to build agents because really, the next frontier is Agentic AI, and we didn't talk a lot about that, but your -- today, we have assisted agents. Tomorrow, these agents are going to -- and you're starting to see them. They're getting very autonomous and you're going to enter at -- what we call frontier organization. So as we think about security, we have to help organizations build these agents. And so we launched our first 11 agents earlier this year, 6 from Microsoft, tackling some of the biggest challenges like phishing and identity and data security that we talked about. And then we have 5 agents, Brad, to your point from the ecosystem. And we're really excited. I mean I think the best part of this is that we are working with the ecosystem. So we have Tanium doing alert triaging agents. We have OneTrust doing privacy agents. We have Aviatrix doing network agents for us. And this just completes the portfolio. So now a customer can get all of it from 1 platform through Copilot and protect themselves. So that's going to be a big focus of ours is getting more partners to develop agents.

Makes sense. It's a team sport. As I think about Microsoft from the very top down, trust and security are completely paramount. And Microsoft has done work around the secure future initiative that's very, very impressive. We saw a stat released a few months ago, Microsoft has dedicated the equivalent of 34,000 engineers working full time for 11 months to this project. I mean, that's bigger than other massive scale companies, let alone a single project. Can you tell us a bit more about this and why it's so important?

So Microsoft started the journey of the Secure Future Initiative years back in 2024. And actually in 2023 fall is when we introduced and then we doubled down. The reason Secure Future Initiative is important is for all the reasons we just talked about. We are in the age of AI. We are in the age of Agentic AI, and the way we have defended organizations even 5 years back is different than how we need to defend organizations going forward. The Secure Future Initiative was born out of the need for defending first Microsoft, learning through that and then protecting our customers and our ecosystem for this age of AI. There were some very challenging attacks related to nation-state actors that we are facing. And we had to rethink about how do we make sure that security is a priority, not just for the security team, but for every single person at Microsoft. And as much as it is an engineering transformation, it is a cultural transformation. And we are hoping that we can use this blueprint to help our customers and to help our ecosystem in general. Secure Future Initiative is based on Zero Trust principle. So that is a I think a lasting framework for the security industry, which starts with verifying explicitly, having least privileged access and assuming breach. And the 3 principles are secured by design. So every line of code that you write has to be secure from the [indiscernible], secure by default, security should be out of box. You should not be bolting it on. And then secure operations all around, if we want to secure that. We have 6 engineering pillars, everything from protecting your network, your identities, your engineering systems, your tenants and production systems, making sure the accelerating response and remediation to making sure we are continuously monitoring and detecting threats. And as you said, we have 34,000 engineers. One of the most important things about Secure Future Initiative is our CEO declared this as the #1 priority. Security is the #1 priority for Microsoft, so above all else, because without security, you cannot have trust. And every single employee at Microsoft now has a security priority. A compensation, it's tied to that. We review our progress with Satya, our CEO every 2 weeks. We send a report every week and of course, with the Board from a governance standpoint. So it's a pretty big initiative. And they're using Secure Future Initiative to really turbo charge our flywheel of defense, this portfolio that you see. So as we learn, we're using all of those things and those investments to make sure that we are building products with those innovations so that we can then protect our customers.

Awesome. That is great. I wanted to turn for a moment to what's happening in security operations. And what's really caught our eye of late is just the innovation that Microsoft is driving within the SOC with Sentinel and especially in the midst of a number of other cyber platforms that are [indiscernible] to displace legacy SIEM solutions out there. Would love to hear how you view the SOC evolving and where the most interesting opportunities are?

Yes. And thank you for saying the kind words about our innovation. It's been a journey for us. And if you look at even the security operations market, it's getting disrupted, like categories are getting disruptive. And by the way, GenAI is going to be a major category disruptor. What we saw early on is SIEM, XDR, like SOAR, UEBA, you have all these like acronyms in the security industry. There are a lot of acronyms. But they're all really trying to do the same thing, which is secure organizations, give them visibility and provide that -- find that needle in the haystack. So what we did was we combined what was extended detection and response. So protecting your e-mail, protecting your endpoint, protecting your cloud apps, protecting your identities all into one. And then we took SIEM, which is your security information event management, and we brought them together. So we were the first ones in the industry to bring XDR and SIEM together. And we are using now that with, of course, with GenAI and AI and machine learning to turbocharge that. We're also integrating security Copilot into that. So we have -- we are seeing like advances in that. And we have just announced a recent data lake, so not only do you have analytics and the hot tier, but you also have cold tier because one of the things we hear from our customers is, it's really expensive to have SIEMs to all of this data. So that's the convergence we have been driving. We are leaders in both SIEM and XDR. So we are best-of-breed in those. And now with GenAI, I think we're going to see a tremendous amount of just innovation and leapfrogging across that. So it's been -- it's been great to see that. And these are very big businesses for us as well, as you know, Brad, like we have shared publicly that just our Sentinel business is $1 billion. And 2 years back, we shared across like our Security business is more than $20 billion in revenue, which is a big number.

It's a big number. And as we talk about the SOC, Sentinel in our conversation with CISO is a foundational element of their strategy. So it's not surprising. I wanted to turn to a different topic. Palo Alto Networks recently announced the acquisition of CyberArk, which I think really shines the light on the importance of identity security. Not that it always wasn't important. But I mean, they're making a call that now is an inflection point. Microsoft already has a very strong presence with Entra. But as competitors expand from their core competencies, how you think about the architectural center of gravity or capabilities around which identity strategies will coalesce.

Yes. And I always believe that an investment by any company in security is a good investment for the security industry because we -- it's challenging for all of us. I feel like we are all on the same side. But also it reinforces our strategy in so many ways because we started with identity. We believe identity is the boundary in this boundaryless world, right? I mean even going back to the pandemic and remote work and all of that, it was identity. And it is the first thing we do as you log in, you log into a system. Our old CISO used to say, attacker don't break in, they log in. So it just kind of reemphasizes how important identity continues to be. And we've been on this identity journey, Brad, like you've been following us for many, many years. We started with identity management, with Entra, we extended to Protection and conditional access, which is really our policy engine at the heart of it. and then, of course, Identity Governance more recently and then [indiscernible]. So we have the entirety of the identity portfolio. It is very critical for our security portfolio, see Entra there. And it's very critical because for Zero Trust because Zero Trust really starts with identity. So this is a big place where we're going to make investments. The other thing I'd say is in the age of Agentic AI, identity, again, becomes that tip of the spear because these agents, these autonomous agents are all going to need their own identity, and we announced just a couple of months back. that we have agent ID. So Entra now can provide agents IDs so that we can now have them and they're doing autonomous work, track them just like we would in humans. So I think expect to see a lot more innovation from us in the identity side.

We look forward to it. So Vasu, we've talked about AI security. We've talked about the SOC. Where else does Microsoft see opportunity within the broader security landscape?

Yes, we covered a lot of ground. I think what we're seeing is that consolidation is going to be continuously a major theme for us. Ultimately, security is going to be about simplification. It is going to be about GenAI. And it's going to be about making sure these key areas like identity, cloud security, endpoint security, network security, apps are secured end-to-end. But more than that, if I were just to step back and look at the security market, it is changing pretty rapidly. It is going to be the foundation of trust for all organizations, especially as we embark on AI, we can't really do AI without security. We just can't. And so I think where we are seeing this going is security, governance, privacy, observability are all coming together in security as a whole and that end-to-end protection matters. You've got to secure AI, you've got to secure end-to-end and you have to secure it using GenAI. So those are the big trends. And we're monitoring the market and hopefully shaping it actively as well.

Makes sense. Perhaps I'd be dramatic or oversimplifying to say that without trusted security, there is no Microsoft.

There is no Microsoft. Microsoft runs on trust and trust cannot happen without security. It's the first thing you need to do because if you live in a house and if you're worried about thiefs coming into your house every single day. I don't think we could cook the meal or watch the television or do all work. That's what security is. Security is the threat actors knocking on your door. And not only that, being in your homes and your kitchen, we don't even know that. So how can we can live our lives? How can we use technology without security? That's a dystopian world, and that's how critical security is it has to come above all else.

But with that said, and since we are here at an investor conference, how does this all drive shareholder value perhaps directly and indirectly?

Yes. I mean it's absolutely -- it's deeply tied to that because if you think about it, security is 1 of the top priorities for all organizations, full stop. It's consistently been in the top 2, top 3. It's the most defensible spend. There's no organization which is going to say, I want to cut Your security spend and put on anything else because what happens if you get attacked? What happens if you get breached and the trust that you lose? How much time? I talked about that $9 trillion number. Just think about the economic impact. We know businesses, which could not come back online due to a breach. So it's that's how critical security is and that's directly tied to share holder value. And then we talked a lot about innovation, that's customer value. When we provide value to our customers, they can do what they're supposed to do and that keeps that whole flywheel of innovation and shareholder value, I think, keeps spinning. So I think security is going to be probably even more critical going forward for GenAI than it was because you cannot do AI without Security.

Vasu, this is a really great session to better understand Microsoft security and how important it is for the overall company. And frankly, for the work that you do, that benefits the entire industry and all of us here. Any final thoughts that you want to leave us with?

The 1 thing I would say is, please, please check out what Microsoft is doing on GenAI security. We have, of course, published some information but this entire portfolio that you're seeing right now is being used to secure AI. We're already securing 2 million apps with Defender. And if you just look at going back to your shareholder values question as well. Our Purview, which is data security, it becomes critical for the AI world, we have 75% of our security attached to Copilot. So there's a lot of work that Microsoft is doing in GenAI and it would be great to get your thoughts on it as well.

Excellent. Well, with that, thank you again for being here.

Thank you, Brad. Thank you. It's been a pleasure.

Greetings, and welcome to the Microsoft Fiscal Year 2025 Fourth Quarter Earnings Conference Call.

[Operator Instructions] As a reminder, this conference is being recorded.

It is now my pleasure to introduce Jonathan Neilson, Vice President of Investor Relations.

Good afternoon, and thank you for joining us today. On the call with me are Satya Nadella, Chairman and Chief Executive Officer; Amy Hood, Chief Financial Officer; Alice Jolla, Chief Accounting Officer; and Keith Dolliver, Corporate Secretary and Deputy General Counsel.

On the Microsoft Investor Relations website, you can find our earnings press release and financial summary slide deck, which is intended to supplement our prepared remarks during today's call and provides a reconciliation of differences between GAAP and non-GAAP financial measures. More detailed outlook slides will be available on the Microsoft Investor Relations website when we provide outlook commentary on today's call.

On this call, we will discuss certain non-GAAP items. The non-GAAP financial measures provided should not be considered as a substitute for or superior to the measures of financial performance prepared in accordance with GAAP. They are included as additional clarifying items to aid investors in further understanding the company's fourth quarter performance in addition to the impact these items and events have on the financial results.

All growth comparisons we make on the call today relate to the corresponding period of last year, unless otherwise noted. We will also provide growth rates in constant currency when available as a framework for assessing how our underlying businesses performed, excluding the effect of foreign currency rate fluctuations. Where growth rates are the same in constant currency, we will refer to the growth rate only.

We will post our prepared remarks to our website immediately following the call until the complete transcript is available. Today's call is being webcast live and recorded. If you ask a question, it will be included in our live transmission, in the transcript and in any future use of the recording. You can replay the call and view the transcript on the Microsoft Investor Relations website.

During this call, we will be making forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could materially differ because of factors discussed in today's earnings press release and the comments made during this conference call and in the Risk Factors section of our Form 10-K, Forms 10-Q and other reports and filings with the Securities and Exchange Commission. We do not undertake any duty to update any forward-looking statement.

And with that, I'll turn the call over to Satya.

Thank you, Jonathan. It was a very strong close to what was a record fiscal year for us. All up, Microsoft Cloud surpassed $168 billion in annual revenue, up 23%.

The rate of innovation and the speed of diffusion is unlike anything we have seen. To that end, we are building the most comprehensive suite of AI products and tech stack at massive scale. And to provide more context, I want to walk up the stack, starting with Azure.

Azure surpassed $75 billion in annual revenue, up 34%, driven by growth across all workloads. We continue to lead the AI infrastructure wave and took share every quarter this year. We opened new DCs across 6 continents and now have over 400 data centers across 70 regions, more than any other cloud provider.

There is a lot of talk in the industry about building the first gigawatt and multi-gigawatt data centers. We stood up more than 2 gigawatts of new capacity over the past 12 months alone. And we continue to scale our own data center capacity faster than any other competitor.

Every Azure region is now AI-first. All of our regions can now support liquid cooling, increasing the fungibility and the flexibility of our fleet. And we are driving and riding a set of compounding S curves across silicon, systems and models to continuously improve efficiency and performance for our customers.

Take, for example, GPT4o family of models, which have the highest volume of inference tokens. Through software optimizations alone, we are delivering 90% more tokens for the same GPU compared to a year ago.

Beyond the AI fleet, we continue to build our commercial cloud to address customers' unique data residency and sovereignty requirements. This quarter, we introduced the Microsoft Sovereign Cloud, the industry's most comprehensive solution spanning both public and private cloud deployments.

All of this innovation is driving our strong results. We saw accelerating growth from migrations again this quarter. Nestle, for example, migrated more than 200 SAP instances, 10,000-plus servers, 1.2 petabytes of data to Azure with near 0 business disruption. That makes it one of the largest and most successful migrations in business history.

The next big accelerator in the cloud will be Quantum, and I'm excited about our progress. In fact, earlier this month, we announced the world's first operational deployment of a Level 2 Quantum computer in partnership with Atom Computing. This is how we will continue to think and make investments, with decade-long arcs, while making progress every quarter.

The next layer is data, which is foundational to every AI application. Microsoft Fabric is becoming the complete data and analytics platform for the AI era, spanning everything from SQL to no-SQL, to analytics workloads. It continues to gain momentum with revenue up 55% year-over-year and over 25,000 customers. It's the fastest-growing database product in our history.

Fabric OneLake spans all databases and clouds, including semantic models from Power BI, and therefore, it is the best source of knowledge and grounding for AI applications and context engineering.

Azure Databricks and Snowflake on Azure both accelerated as well. Cosmos DB and Azure PostgreSQL are both powering mission-critical workloads at scale. OpenAI, for example, uses Cosmos DB in the hot path of every ChatGPT interaction, storing chat history, user profiles and conversational state. And Azure PostgreSQL stores metadata critical to the operation of ChatGPT as well as OpenAI's developer APIs.

This year, we launched Azure AI Foundry to help customers design, customize and manage AI applications and agents at scale. Foundry features best-in-class tooling, management, observability and built-in controls for trustworthy AI. Customers increasingly want to use multiple AI models to meet their specific performance, cost and use case requirements. And with Foundry, they can provision inferencing throughput once and apply it across more models than any other hyperscaler, including models from OpenAI, DeepSeek, Meta, xAI's Grok and, very soon, Black Forest Labs and Mistral AI.

We sim-shipped 15 models from OpenAI alone on Foundry this year, providing same-day access to state-of-the-art models deeply integrated with our infrastructure and tools. And we are seeing accelerated adoption of our new Foundry Agent Service, which is now being used by 14,000 customers to build agents that automate complex tasks.

For example, Nasdaq is using foundry to build agents that help customers prepare for Board meetings, cutting prep time by up to 25%. All up, 80% of Fortune 500 already use Foundry. And when we look narrowly at just the number of tokens served by Foundry APIs, we processed over 500 trillion this year, up over 7x. This is a good indicator of true platform diffusion beyond a few head apps and services.

Talking about the app layer, these applications are becoming embedded in our daily work and life. Our family of Copilot apps has surpassed 100 million monthly active users across commercial and consumer. And when you take a broader look at the engagement of AI features across our products, we have over 800 million monthly active users.

Microsoft 365 Copilot is becoming the new way to organize work and workflow and work artifacts. We rolled out our biggest update to Microsoft 365 Copilot to date this quarter, bringing together chat, search, create, notebooks as well as agents into one intuitive scaffolding.

With this innovation and continued product improvements, we are seeing real momentum. Customers continue to adopt Copilot at a faster rate than any other new Microsoft 365 suite, with strong usage intensity as shown by our week-over-week retention. And we saw the largest quarter of seat adds since launch with a record number of customers returning to buy more seats.

Barclays, for example, will roll out Microsoft 365 Copilot to 100,000 employees globally following a successful initial deployment of 15,000. UBS is expanding its deployment to all of its employees after initially rolling it out to 55,000 of them. And Adobe, KPMG, Pfizer, Wells Fargo all purchased over 25,000 seats this quarter.

Tens of thousands of organizations have already used our Researcher and Analyst deep reasoning agents in the first weeks of availability. And we have introduced group-level agents in Teams like Facilitator and Interpreter, which generate real-time translation and notes in meetings. Hundreds of partners like Adobe, SAP, ServiceNow and Workday have built their own third-party agents that integrate with Copilot and Teams.

We are also seeing more customers use Copilot Studio to extend Microsoft 365 Copilot and build their own agents. This year, customers created 3 million agents using SharePoint and Copilot Studio. And with Copilot Tuning, they can easily create agents fine-tuned on their company's data, workflow and style that reflect their unique tone, language and expertise.

We're also seeing great traction among specific roles and functions, starting with developers. GitHub Copilot continues to have great momentum in IDE with Agent Mode and new form factors like Coding Agent which is capable of asynchronously executing developer tasks. We have 20 million GitHub Copilot users. GitHub Copilot enterprise customers increased 75% quarter-over-quarter as companies tailor Copilot to their own codebases, and 90% of the Fortune 100 now use GitHub Copilot.

More broadly, GitHub usage and repos are seeing explosive growth because of AI. AI projects on GitHub more than doubled over the last year. The surge in vibe coding projects and AI coding agents, whether it is Claude Code, Codex, Cursor or GitHub Copilot, are generating more pull requests and more repos on GitHub. And our Code Review Agent is being used heavily across the platform, performing millions of code reviews each month.

In health care, we had a breakout year for Dragon Copilot. Customers used our ambient AI solutions to document over 13 million physician-patient encounters this quarter, up nearly 7x year-over-year.

For example, at Mercyhealth system, more than 1,000 physicians are already using Copilot to reduce administrative burden, so that they can focus on providing better care. They have saved more than 100,000 hours to date and plan to expand to all 5,000 providers. As one physician put it, "The best thing to happen to my practice in 10 years."

And in security, we were the first in the industry to introduce agents to help defenders autonomously handle high-volume security and IT tasks. More broadly, AI is driving a fundamental change in the biz apps market as customers shift from legacy systems to agentic business applications.

Dynamics 365 took share this year. And we are winning customers in every industry, like Verizon with Sales, Domino's Pizza Group with ERP, 1-800-Flowers with Contact Center.

When it comes to consumer apps, we are innovating across all surfaces. In fact, on Monday, we introduced Copilot Mode in Edge. It's especially exciting to see the innovation coming back to browsers. Copilot Mode brings together Copilot composer, chat, discover, search and actions to build the next generation of browser for the AI age.

Our Copilot consumer app also continued to see strong growth in engagement and successful sessions. And we are bringing Copilot to every Windows 11 PC. With Copilot Vision, you can share your screen with Copilot and get real-time insights and assistance on anything.

And we are well positioned as we approach Windows 10 end of support in October, thanks to Windows 11 and Copilot+ PCs, which offer customers compelling security as well as AI value.

Talking about security, it underlies our cloud and AI infrastructure as well as our Copilots and agents. We have launched over 100 new capabilities over the past year. Just last week, we added a modern data lake to our SIEM Microsoft Sentinel, bringing together customer data across our first-party tools as well as third-party ecosystem over 350 connectors.

We are also extending the systems customers already use for governance, identity, security and management to protect every AI agent. Entra now extends identity permissions, policies and access controls to agents. Defender secures nearly 2 million general AI apps. Purview is used by 3/4 of the Microsoft 365 Copilot customers to protect their data. And all up, we now have nearly 1.5 million security customers and continue to take share across all major categories we serve.

Before I wrap, I want to talk about 2 consumer businesses of ours with massive end-user reach: LinkedIn and Xbox. LinkedIn is home to 1.2 billion members with 4 consecutive years of double-digit member growth. All up, comments on LinkedIn rose over 30% and video uploads increased over 20% this year. We continue to bring AI to every part of LinkedIn experience, introducing agents across hiring as well as sales.

When it comes to Gaming, we have 500 million monthly active users across platforms and devices. We are now the top publisher on both Xbox and PlayStation this quarter with successful launches of Forza Horizon 5 and Oblivion Remastered. The Call of Duty franchise has never been stronger. 50 million people have played Black Ops 6; total hours surpassed $2 billion. Minecraft saw record monthly active usage in revenue this quarter, thanks in large part to the success of the Minecraft Movie.

And we have nearly 40 games in development, so much, much more to come. We surpassed over 500 million hours of gameplay stream via the cloud this year. And Game Pass annual revenue was nearly $5 billion for the first time.

In closing, we are going through a generational tech shift with AI, and I have never been more confident in Microsoft's opportunity to drive long-term growth and define what the future looks like.

With that, let me turn it over to Amy to walk through our financial results as well as the outlook.

Thank you, Satya, and good afternoon, everyone. This year, we delivered over $281 billion in revenue, up 15% year-over-year, which reflects the broad strength of our products and services. Operating income was over $128 billion, up 17% year-over-year as we invested against the expansive opportunity ahead.

And in our largest quarter of the year, we significantly exceeded expectations with strong execution by our sales and partner teams. As Satya shared, we're innovating faster than ever to deliver new value to our customers.

This quarter, revenue was $76.4 billion, up 18%, and 17% in constant currency. Gross margin dollars increased 16%, and 15% in constant currency, while operating income increased 23%, and 22% in constant currency. And earnings per share was $3.65, an increase of 24%, and 22% in constant currency.

For the first time, commercial bookings were over $100 billion, increasing 37%, and 30% in constant currency, on a strong prior year comparable. Strong execution across our core annuity sales motions, including healthy renewals, as well as an increase in the number of $10 million and $100 million-plus contracts for both Azure and Microsoft 365 helped drive these results.

Commercial remaining performance obligation increased to $368 billion, up 37% and 35% in constant currency. Roughly 35% will be recognized in revenue in the next 12 months, up 21% year-over-year. The remaining portion, recognized beyond the next 12 months, increased 49%. And this quarter, our annuity mix was again 98%.

FX was roughly in line with expectations on total company revenue, segment level revenue, COGS and operating expense growth.

Microsoft Cloud revenue was $46.7 billion, ahead of expectations, and grew 27%, and 25% in constant currency. Microsoft Cloud gross margin percentage was slightly better than expected at 68%, down 2 points year-over-year from the impact of scaling our AI infrastructure, partially offset by continued efficiency gains in Azure and M365 Commercial Cloud.

Company gross margin percentage was 69%, down 1 point year-over-year, driven by sales mix shift to Azure and the lower Microsoft Cloud gross margin noted earlier. Operating expenses increased 6% and 5% in constant currency, and operating margins increased 2 points year-over-year to 45%. Better-than-expected revenue growth coupled with a focus on operating efficiently drove the margin expansion.

At a total company level, head count at the end of June was relatively unchanged year-over-year.

Now to our segment results. Revenue from Productivity and Business Processes was $33.1 billion and grew 16%, and 14% in constant currency, better than expected, driven by M365 Commercial products in cloud services and M365 Consumer products in cloud services. M365 Commercial Cloud revenue was ahead of expectations and increased 18%, and 16% in constant currency, with 2 points of benefit from in-period revenue recognition.

Business trends remained relatively stable to the prior quarter when excluding the in-period revenue recognition, with ARPU growth again driven by E5 and M365 Copilot. Paid M365 commercial seats grew 6% year-over-year with installed base expansion across all customer segments, though primarily in our small and medium business and frontline worker offerings.

M365 Commercial Products revenue increased 9% and 7% in constant currency, ahead of expectations due higher-than-expected Office 2024 transactional purchasing. M365 Consumer cloud revenue was better than expected, increasing 20% driven by ARPU growth following the January price increase and subscriber growth of 8%.

LinkedIn revenue increased 9%, and 8% in constant currency, with growth across all businesses, though Talent Solutions continues to be impacted by weakness in the hiring market. Dynamics 365 revenue increased 23%, and 21% in constant currency, with strong execution in our core annuity sales motions leading to growth across all workloads.

Segment gross margin dollars increased 16% and 15% in constant currency, and gross margin percentage increased slightly, driven by the efficiency gains noted earlier, even as we deliver more AI features across our products and scale our AI infrastructure.

Operating expenses increased 7%, and 6% in constant currency. And operating income increased 21%, and 19% in constant currency.

Next, the Intelligent Cloud segment. Revenue was $29.9 billion and grew 26%, and 25% in constant currency, ahead of expectations, driven by Azure and our on-premises server business. In Azure and other cloud services, revenue grew 39%, significantly ahead of expectations, driven by accelerated growth in our core infrastructure business, primarily from our largest customers. As a reminder, new cloud and AI workloads are built and scaled using the breadth of our services.

Revenue from Azure AI services was generally in line with expectations. And while we brought additional data center capacity online this quarter, demand remains higher than supply.

In our on-premises server business, revenue decreased 2%, and 3% in constant currency, ahead of expectations, primarily driven by transactional purchasing which also has higher in-period revenue recognition. Enterprise and Partner Services revenue increased 7%, and 6% in constant currency, with growth in Enterprise Support Services partially offset by a decline in Industry Solutions.

Segment gross margin dollars increased 17%, and 16% in constant currency, and gross margin percentage decreased 4 points year-over-year driven by scaling our AI infrastructure, partially offset by Azure efficiency gains noted earlier. Operating expenses increased 6%, and 4% in constant currency. And operating income grew 23%.

Now to More Personal Computing. Revenue was $13.5 billion and grew 9%, exceeding expectations, primarily due to Windows OEM as well as Xbox content and services. Windows OEM and Devices revenue increased 3% year-over-year, ahead of expectations, as inventory levels remained elevated.

Search and news advertising revenue ex TAC increased 21% and 20% in constant currency, driven by continued growth in both volume and revenue per search, as well as roughly 8 points of favorable impact from third-party partnerships, including the benefit of a low prior-year comparable.

And in Gaming, revenue increased 10%. Xbox content and services revenue increased 13%, and 12% in constant currency, driven by better-than-expected performance from first-party content and Xbox Game Pass.

Segment gross margin dollars increased 15%. Gross margin percentage increased 3 points year-over-year with improvement across all businesses. Operating expenses increased 4%, and 3% in constant currency. Operating income increased 34%, and 33% in constant currency, driven by continued prioritization of higher margin opportunities.

Now back to total company results. Capital expenditures were $24.2 billion, including $6.5 billion of finance leases where we recognize the full value at the time of lease commencement. Cash paid for PP&E was $17.1 billion. The difference is primarily due to finance leases. More than half our spend was on long-lived assets that will support monetization over the next 15 years and beyond. The remaining spend was primarily for servers, both CPUs and GPUs, and driven by strong demand signals.

Cash flow from operations was $42.6 billion, up 15%, driven by strong cloud billings and collections, partially offset by higher supplier payments. And this quarter, free cash flow was $25.6 billion.

Other income and expense was negative $1.7 billion, primarily due to losses on investments accounted for under the equity method. Our effective tax rate was approximately 17%.

And finally, we returned $9.4 billion to shareholders through dividends and share repurchases, bringing our total cash return to shareholders to over $37 billion for the full fiscal year.

Now moving to our outlook. My commentary for both the full year and next quarter is on a U.S. dollar basis, unless specifically noted otherwise. Let me start with some full year commentary for FY '26.

First, FX. Assuming current rates remain stable, we expect FX to increase full year revenue growth and COGS growth by approximately 2 points and to increase operating expense growth by 1 point.

Next, building on the strong momentum we saw this past year, we expect to deliver another year of double-digit revenue and operating income growth in FY '26. We will continue to invest against the expansive opportunity ahead across both capital expenditures and operating expenses given our leadership position in commercial cloud, strong demand signals for our cloud and AI offerings, and significant contracted backlog.

Capital expenditure growth, as we shared last quarter, will moderate compared to FY '25 with a greater mix of short-lived assets. Due to the timing of delivery of additional capacity in H1, including large finance lease sites, we expect growth rates in H1 will be higher than in H2.

We remain focused on delivering revenue growth and increasing our operational agility. And as a result, we expect operating margins to be relatively unchanged year-over-year. And finally, we expect our FY '26 effective tax rate to be between 19% and 20%.

Now to our outlook for the first quarter. Based on current rates, we expect FX to increase total revenue growth by 2 points. Within the segments, we expect FX to increase revenue growth by roughly 3 points in Productivity and Business Processes and roughly 1 point in Intelligent Cloud and More Personal Computing. We expect FX to increase COGS and operating expense growth by roughly 1 point.

In Commercial bookings, we expect healthy growth on a growing expiry base. Bookings growth will again be driven by strong execution across our core annuity sales motions and long-term commitments to our platform. As a reminder, larger long-term Azure contracts, which are more unpredictable on their timing, drive increased quarterly volatility in our bookings growth rate.

Microsoft Cloud gross margin percentage should be roughly 67%, down year-over-year, driven by the impact of continuing to scale our AI infrastructure.

We expect Q1 capital expenditures to be over $30 billion, driven by the continued strong demand signals we see. As a reminder, there can be quarterly spend variability from cloud infrastructure build-outs and the timing of delivery of finance leases.

Next, to segment guidance. In Productivity and Business Processes, we expect revenue of USD 32.2 billion to USD 32.5 billion, or growth of 14% to 15%, with roughly 3 points of benefit from FX as noted earlier. In M365 Commercial Cloud, we expect revenue growth to be between 13% and 14% in constant currency, with business trends that remain relatively stable compared to the prior quarter. ARPU growth will again be driven by E5 and M365 Copilot.

M365 Commercial Products revenue growth should be in the mid- to high single digits. As a reminder, M365 Commercial Products includes both the Windows Commercial on-premises components of M365 suites and Office transactional purchasing, both of which can be variable due to in-period revenue recognition dynamics.

M365 Consumer Cloud revenue growth should be in the low 20s, driven by the January price increase. For LinkedIn, we expect revenue growth in the high single digits. And in Dynamics 365, we expect revenue growth to be in the high teens with continued growth across all workloads.

For Intelligent Cloud, we expect revenue of USD 30.1 billion to USD 30.4 billion, or growth of 25% to 26%, with roughly 1 point of benefit from FX as noted earlier. Revenue will continue to be driven by Azure, which can have quarterly variability in year-on-year growth rates depending on the timing of capacity delivery and when it comes online, as well as from in-period revenue recognition depending on the mix of contracts.

In Azure, we expect Q1 revenue growth of approximately 37% in constant currency, driven by strong demand for our portfolio of services on a significant base. Even as we continue bringing more data center capacity online, we currently expect to remain capacity-constrained through the first half of our fiscal year.

In our on-premises server business, we expect revenue to decline in the low to mid-single digits with the ongoing customer shift to cloud offerings. In More Personal Computing, we expect revenue to be USD 12.4 billion to USD 12.9 billion.

Windows OEM and Devices revenue should decline in the mid- to high single digits. We expect the elevated inventory levels at the end of Q4 to come down through the quarter in Windows OEM, although the range of potential outcomes remains wider than normal. Devices revenue should decline.

Search and news advertising ex TAC revenue growth should be in the low to mid-teens, down sequentially as growth rates normalize following the benefit from third-party partnerships noted earlier. Growth will continue to be driven by volume and revenue per search across Edge and Bing. Overall search and news advertising revenue growth should be in the low double digits.

And in Gaming, we expect revenue to decline in the mid to high single digits. Against a strong prior year comparable, we expect Xbox content and services revenue to decline in the mid-single digits.

Now back to company guidance. We expect COGS of USD 24.3 billion to USD 24.5 billion, or growth of 21% to 22%, and operating expense of USD 15.7 billion to USD 15.8 billion, or growth of 5% to 6%. Other income and expense is estimated to be negative $1.3 billion, primarily due to investments accounted for under the equity method. As a reminder, we do not recognize mark-to-market gains or losses on equity method investments.

And lastly, we expect our Q1 effective tax rate to be between 19% and 20%.

In closing, we finished the year with double-digit revenue and operating income growth and exceeded the FY '25 operating margin commitment we shared a year ago. Our focus remains on investing in security, quality and AI platform and product innovation that delivers value and opportunity to our customers. We are excited for FY '26.

With that, let's go to Q&A, Jonathan.

Thanks, Amy. We'll now move over to Q&A. [Operator Instructions] Operator, can you please repeat your instructions?

[Operator Instructions] And our first question comes from the line of Keith Weiss with Morgan Stanley.

Congratulations on a fantastic end to FY '25. I've been covering Microsoft for a while, I don't think I've ever seen a quarter where like everything came together this well. So congratulations on that execution.

Maybe a little bit longer-term focused question to start out. You guys have always had software start-ups as customers and potentially emerging competitors. But the AI labs now feel different. Investors are ascribing valuations which assume these companies become major players in software. They're underwriting revenue forecasts measured in the tens of billions, if not hundreds of billions. And these start-ups have also grown to be some of your biggest customers, so they're contributing significantly to Microsoft today.

So it seems like there's a lot of potential opportunity in supporting those businesses, but also it's not certain that they're going to stay your customers as they scale. They could in-source some of that infrastructure. And they very likely emerge as potential competitors. So as managers of Microsoft, as managers of the capital, how do you guys manage that risk versus reward with these quickly emerging AI labs and these AI start-ups?

Thanks, Keith, for the question. I don't think it's that different from even sort of the previous era. There's always been, I'll call it, head apps or head -- new companies that emerge, that in fact are very needed in order to birth a new platform.

Back in the day when I was getting started on Azure, I used to look over the lake and sort of see Netflix and Amazon, and I say, "I wish Netflix ran on Azure." And in some sense, that's kind of what we now have, which is the largest AI workloads run on Azure.

And when that happens, you learn the workload faster, you optimize the entire platform faster, everything from what you're doing -- what we're doing with Cosmos DB for a chat interface like ChatGPT or Copilot, is, guess what, going to be the most relevant for any AI application going forward.

The entire data stack that we have now built is going to be optimized for what people describe as one of the hardest challenges of any AI application, called context engineering, right, which is how do you collect your data and then make sure that the context around the problems remain stable over a long period so that you get the intelligence to actually deliver the results you want. So these are workload results that are invaluable for us to learn to build both the products as well as the platform.

And then broadly, they -- or rather over time, there will be broad diffusion. In fact, one of the things that Amy and I track is not just the head app usage, but also what's the sort of all the Tier 2 applications that are being built.

So that sort of -- that speaks a little bit, Keith, to I think your question, is as long as we have head apps shaping the platform and then, after that, we have the broad diffusion happen, which in some sense both of those is what we are seeing. So I feel very good about our being in decent standing going forward.

The next question comes from the line of Mark Moerdler with Bernstein Research.

I'd also give you my congratulations. Amazing. I didn't know how you were going to beat last quarter, and you did it. So congratulations.

Satya and Amy, we're now 2-plus years into the GenAI revolution and adoption is still early and ramping. What do you think is the best way that software companies are going to be able to monetize AI for SaaS? Do you believe there are differences in monetization for horizontal, more general apps like M365 Copilot or Dynamics CRM Copilot versus very targeted capacities on the agentic side? And also, how should you think about the trajectory of SaaS AI margins over the long term?

Yes. I'll start, and Amy, you should feel free to add. I mean if I just broaden out beyond just SaaS as a category, I think just like the server-to-cloud transition was an expansion of essentially usage of servers. That is essentially what happened with the cloud, right, which is we did a bunch of servers except that the expertise required, the capital required, the time required to bring up servers, build it out, scale it, was just all hard. And so therefore, the market was a certain size.

Whereas with the cloud, you could sort of buy it with flexibility, you could burst and you could spin up and spin down, the expertise required came down, so it was just -- orders of magnitude.

That's what's happening. So if you sort of even subscribe to this point of view that intelligence is basically log of compute, that means compute is going to grow and you've got to use it as efficiently as possible to just keep creating intelligence.

Now how does it manifest beyond just the infrastructure? I kind of, to Keith's earlier question, I talked a little bit about how the infrastructure is getting shaped, data layer is getting shaped, the app server is getting built. These are all classic categories of infrastructure that will continue, but there will be an order or 2 of magnitude more.

So literally, like -- in fact, one of the other things we track is every GPU requires storage and compute. That ratio is another thing that is really exponential for infrastructure growth.

So when you go to the app layer, the SaaS apps themselves are now building in effectively agentic and chat interfaces with intelligence. And they're also building autonomous agents.

Agents are kind of like applications, like a database application perhaps, but they are being used increasingly inside of a user interaction. I think a good example is GitHub Copilot. It got started as code completions on an IDE. Then we added the chat interface to it. Then we added the Agent Mode to it. And now we have an autonomous agent, which in fact works completely asynchronously, right? So all those 4 things are now part of essentially GitHub.

And by the way, it also turns out that every other tool that is also doing any form of coding is adding more and more GitHub repos. So if I have to think about GitHub monetization, we have an opportunity around just monetizing GitHub Enterprise and then we have the ability to think about GitHub Copilot, and GitHub Copilot as with all these form factors.

And so that's exactly the same thing that's happening with Microsoft 365. That's the same thing that's happening with Dynamics 365. So you have to be very open to taking your data tier, your business logic tier and your UI tier and sort of being more expansive in it. As long as you do that, it's just that usage goes up, and that's what I think shows up in the results.

And I think, Mark, if you wanted to think about all the things and the layers that you talked about, is really we're seeing very similar monetization tools exist in this transition too, right? There's a per user logic, there's tiers of per user. Sometimes those tiers relate to consumption, sometimes there's pure consumption models.

I think you'll continue to see a blending of these. Especially as the AI model capability grows, you'll end up with ways that teams are going to want to throttle that usage, use the best models for the best job. And I think the blending of these models will continue to be something we see on a go-forward basis.

The next question comes from the line of Karl Keirstead with UBS.

Satya and Amy, this is the second quarter in a row of pretty material Azure upside from what sounds like an acceleration in on-prem to Azure migration activity. I'm just wondering if you can comment on the plethora of customer conversations you've had, whether there are a couple of 2 or 3 specific catalysts that are driving that migration. And how durable a trend do you think that is?

Yes. Just 3 things are really happening. One is the migrations. A good example would be what I referenced in my remarks with Nestle, with the SAP instances they moved, along with a lot of the data that's associated with it and a bunch of servers. So that's kind of a classic example. I think, whether it's VMware migrations or migrations of SAP, or even just our own server migrations, they're pretty healthy. And it turns out that we're still not anywhere close to the finish line, if at best, maybe in the middle innings of that.

The second thing that's also happening is cloud-native applications that are scaling. This is even excluding all of the AI stuff, just the classic cloud native e-commerce company, let's say. These are scaling in a big way. And some of those customers were not on Azure previously, but now they're increasingly there, because they have come for AI perhaps but they now stay for more than AI. And so to me, that's another thing you see in overall, what's happening across the Azure number.

And then, of course, there are the new AI workloads. So those are 3 things that are all, in some sense, building on each other, but that's kind of what's driving our growth.

The next question comes from the line of Brent Thill with Jefferies.

Satya, back to the strength across the board in the quarter, was there anything that jumped out at you or surprised you that you didn't think you were going to see but you did see in the quarter? Just the magnitude of upside, I think, had shocked many here.

Yes. I don't know, Brent, if anything really surprised us. But I think what we are noticing in our own build-out of these AI applications and in general is the platform is becoming more than, "Here is the model and here is an API. Make some calls," right? I mean that, in some sense, was a bit of the state-of-the-art maybe even a year ago.

Whereas now you have essentially these very stateful app patterns that are emerging that require quite a bit of rethinking of even the app stack. I mean take even the storage tier stuff, right, the degree of sophistication you have, and hey, how much of an index do you really want to build by preprocessing so that your prompt engineering, or context engineering as I call it, can be better and higher quality?

So I think all of that is emerging. So when I look at a product like Azure Search, Fabric and Cosmos DB, all of the things, the frameworks around it, are just becoming robust to build serious applications.

And so that's what I feel great about, is the learning curve inside the company, outside the company, the diffusion of the stack, the speed with which that's emerging, that you can build applications, is much faster.

I always go back and say, hey, when, I don't know, relational database came out, it took a while for people to build an ERP system, let's say. And this thing, we're kind of building pretty sophisticated applications at a very, very fast clip based on, I think, the degree of maturity that's emerging.

The next question comes from the line of Raimo Lenschow with Barclays.

Congrats from me as well. I had one question on Copilot, and I'm obviously a happy user here at Barclays. If you think about it, the one thing that we're all realizing is that Copilot is the AI part, but data is becoming more and more important. And then from there on, we can start thinking about agents.

Where is the -- what are you seeing in your customer conversations, Satya, about like that understanding that Copilot is actually just the starting point, and then, from there on, it's becoming like much, much broader?

Yes. I think that that's right. Even inside of Copilot, I'm sure you're seeing it, right? You now have Analyst and Researcher, to just talk about 2 examples, and of course, people, all the third-party agents. So yes, there is a lot more of it's just not request, respond. It's about spawning essentially applications that then go do work and come back.

But the UI still remains very important, right, even for asynchronous work. To instruct the asynchronous work, you need UI. And to monitor asynchronous work, you need UI. Maybe different, it may not be a chat interface. And of course, you need a way to then inspect what the asynchronous work is, right?

So even take the example I was giving on GitHub. Even if you're not using GitHub Copilot to create the core check-in or the pull request, interestingly enough, we're seeing massive increase to GitHub Copilot Code Review Agent even if you used maybe Claude Code or whatever else to write the code.

So that's I think what's happening across all of these systems. So you're absolutely right that you need -- it starts with some kind of a UI that's more chat focused, but it quickly goes beyond it. And you see it in M365, you see it in Dynamics 365 and you see it in GitHub.

The next question comes from the line of Kash Rangan with Goldman Sachs.

Amy, I want to acknowledge that, I think a few quarters ago, you said that you'll reach a point in time where you can accelerate Azure while slowing down CapEx. So you did it. But what is the outlook? When I look at the CapEx guidance for the upcoming quarter, certainly, I would view that as a positive indicator of the book of business you have for your cloud services.

But how should we think about the shape of the curve of CapEx vis-a-vis Azure growth rate in the years ahead, particularly as I listen to Satya's comments on the AI stack consuming more and more infrastructure? Are we at a point where we're going to have to continue to do this and we magically wait for inference and applications to kick in and, therefore, create a richer gross margin mix? Congrats on the quarter.

Thanks, Kash. Let me back up and first say, when you think about the full year comments I've made on CapEx as well as the Q1 guidance of over $30 billion, you first have to ground yourself in the fact that we have $368 billion of contracted backlog we need to deliver, not just across Azure but across the breadth of the Microsoft Cloud.

So in terms of feeling good about the ROI and the growth rates and the correlation, I feel very good that the spend that we're making is correlated to basically contracted on the books business that we need to deliver and we need the teams to execute at their very best to get the capacity in place as quickly and effectively as they can.

And so when you look, and we've talked about the growth rate will decline year-over-year, but at its core, our investments, particularly in short-lived assets like servers, GPUs, CPUs, networking storage, is just really correlated to the backlog we see and the curve of demand. And I talked about, my gosh, in January and said I thought we'd be in better supply demand shape by June. And now I'm saying I hope I'm in better shape by December.

And that's not because we slowed CapEx. Even with accelerating the spend and trying to pull leases in and get CPUs and GPUs in the system as quickly as we can, we are still seeing demand improve.

And so I am not as focused, Kash, on trying to pick a date at which revenue growth and CapEx growth will meet and cross. I'm focused on building backlog, building business and delivering capacity, which we are seeing has a good ROI today in terms of our ability to get that done.

So I don't want people to get overly focused on a pivot point. Because when you're in sort of these expansive moments, picking a data point usually means you're going to pick to be too conservative in terms of market share gain and in terms of winning. And so I tend to put my energy more there.

Yes. I think one of the other things, Kash, is that -- I think I said this in the previous earnings as well, which is the difference between a hoster and a hyperscaler is software. And the same is going to be true here. That GPT4o example I gave is all software, right, the optimization even in the last year.

So we know how to use the software skills to take any piece of hardware and make it multiple x better. And so that's kind of where the yield will come. But as Amy said, while you're really going and building out the plant, you don't want to sort of serialize it. You just want to go in and paddle on all of these fronts, and that's sort of what will compound over time.

And I do think it's important, when Satya talks about the software layer, he is talking about, in his comments, to connect this back to the compounding S curves. And so I would remind people, that is something that we saw through the prior cloud transition, it's how we operated through that one. And the same sort of skills and logic done at an even faster pace is what will apply the same transition.

The next question comes from the line of Michael Turrin with Wells Fargo.

Congrats from me as well on the metrics working in concert here. Amy, maybe on margin. Impressive to hear expectations for flat operating margin in the upcoming year as you absorb some of the mix shift towards Azure and some of the more AI-focused offerings.

Can you speak in more detail just around your ability to manage those trade-offs and offset some of the mix shift? And I'm wondering specifically just on any productivity gains you're seeing from leveraging AI internally that you'd highlight, or anything else you'd just mention underpinning the full year expectation there?

Thanks, Michael. I think really the area to focus on is, when you think about margin, I think sometimes people get a lot of energy around cost control as a driver of margin. The other driver is to focus on making sure you deliver a great product that's competitive and innovative and can take share, because that drives revenue. And revenue itself, and revenue growth as you all know better, even perhaps than I do, is a durable way to see margin improvement. It builds on itself.

That being said, the second thing I would point to is really what I talked to Kash a little bit about before, Satya and I both mentioned it, is applying all of our skill set here to deliver efficiencies, whether that's at whatever layer of the stack that exists, the S curves compound, and we are doing that work and we're focused on it at the same time we're doing the build out. So you'll see improvements there even as we continue to invest.

And then, of course, it's about continuing to have great talent here focused on products and opportunities where we have the biggest markets and the most likelihood of success. And so when we have those 3 things happen and the energy is right and the focus is there, it gives me confidence in terms of margin delivery.

But make no mistake, it starts and ends really with product, which is what we're really focused on here, and delivering that to customers.

That all sounds pretty good.

Thanks, Michael. That wraps up the Q&A portion of today's earnings call. Thank you for joining us today, and we look forward to speaking with you all soon.

Thank you.

Thank you.

This concludes today's conference. You may disconnect your lines at this time. Thank you for your participation.

Welcome, everybody. Back-to-back software sessions here to kick off the conference. So I'm Brad Sills, senior analyst on the software team and cover Microsoft and others and very much looking forward to the conversation. Delighted to be welcoming Microsoft to the conference. We're very fortunate to have Matt McBride here with us, who's Corporate VP of the commercial business and CFO of Commercial. So Matt, thank you for joining.

Yes, happy to be here.

Great to have here.

So we'll just get started. Why don't we just start off with a little bit on your background and your role at Microsoft, your area of responsibilities to start it off, please?

Yes, fantastic. Yes. My name is Matt McBride. I'm the Commercial CFO. You should think about that as kind of I have a couple of different additional hats I wear in addition to the CFO. I have all the commercial revenue, over $200 billion, growing super well. And then I have a horizontal role for basically all the capital spend for Microsoft with the exception of like Office space. And then all the cost of goods sold with the exception of Surface and Gaming.

And so commercial consumer, anything having to do with the cloud. And they have the same responsibility for R&D. So you can think about my job as being a typical commercial CFO plus a responsibility I have to advise our senior leadership team, Satya and Amy and others, on how to allocate capital for the most efficient return across the company. And it's been a wild ride a little over 19 years, and it's been super fun to be a part of.

That's great. And why don't we start with Azure, an area of responsibility there? Really impressive growth this past quarter at 35%, exceeding your outlook and your guidance and strong guidance for next quarter. Why don't we just start high level? What are some of the key growth drivers within Azure that you'd like to point out that are driving this type of results? Just remarkable.

Yes. It's been -- we had, like I said, a good quarter. We're very happy with it. I'll start first with things like geography, markets, SMB versus enterprise, things of that nature. From that perspective, the performance has been very broad-based. We're not seeing any particular geography outperform another. We're not seeing a particular industry segment or part of the market that's really outperforming another. So it's been good broad-based sales execution across all those different channels. From a product perspective, I'd start with -- maybe I'll cover 3 things, and I'll start with migrations.

Migrations have been going strong. It's -- people want to say it's very late innings in the cloud game. And in fact, it's not. There's still lots of customers who are in different stages of their digital journey. They're still migrating workloads and they're replatforming on applications, and they're coming to Microsoft to help them do that. And so we've seen tremendous progress there. Additionally, as people think about their data estate, we've been doing quite well in data.

In particular, as customers think about how to get the most out of their data, especially when they think about adopting AI services that are increasingly differentiated using that proprietary data, they're choosing Cosmos DB, a very large, scalable, high transaction volume solution for that type of need. And then lastly, our analytics products are doing quite well. And so again, great progress there, and we've seen good progress on AI as well.

Excellent. Excellent. Why don't we dig into AI specifically, if we could, please? Can you talk about some of the traction you're seeing with Azure AI services? And just generally, when you look across the Azure stack, where are you seeing the AI adoption cycle manifest in the product?

Yes, yes, sure. First of all, I'll zoom out a little bit and just say like Microsoft's total set of solutions for AI from Azure plus all of our Copilots is doing quite well broadly. And I think the pitch to customers is that broad pitch in addition to what you want to talk about with just Azure. And we've shared that 85% of the Fortune 500 have adopted some form and are using some form of Microsoft AI services. And it's tremendous progress. I think customers are increasingly looking at how do they go about doing different tasks. And in some cases, it might be improving the employee experience.

It might be helping them deliver new customer value, customer engagement, business process engineering. And then for them, they might be at different places in the AI adoption and innovation curve, and then we want to accelerate that curve for the customer. And we see those 4 things happening pretty regularly when customers are adopting AI. I'll touch briefly on Foundry for a second. Foundry has over 70,000 customers already. And these customers are all different shapes and sizes. And I'll give you a couple of examples here of people who are using these services. Just down the road, there's Stanford Health Center.

And they've gone into Foundry and to use -- to solve a solution and solve a problem. And one of the problems they have is they have to come up with ways to get aligned care for people with cancer. And this process they have is called the tumor board. And it's very manual. It has lots of different role types from data scientists to clinicians to stitch together what they need to be able to go understand and align the care and make a plan to move forward. So what they did is they went into AI Foundry. We have a service in our catalog called Health Service Orchestrator -- Health Agent Orchestrator. And they use that to stitch together all those different sources, all the different role types to basically go out and automate that process.

And that matters a lot, especially in the space when you talk about health. That time savings for all those people is now spent on getting to a faster plan. So the patient is getting care faster. And then the clinicians are saving time that they can then spend on saving more lives. And so that's one, again, very serious life-saving example. I'm not trying to lay it on too thick, but like it's a real example. Maybe a lighter weight example is Ernst & Young. They're providing tax services around the world. And you think about that as a problem. That's lots of documentation, lots of localized regulation and a practice that's, again, global in nature.

And so what they've done is gone into Copilot Studio, and they've created agents and capability to go reason over 21 million documents. I mean it's huge scale over many, many years, again, localized so that now when their tax professionals need information, they can go query, again, in natural ways, natural language ways. And the same is true for the Stanford example. We're talking about Teams, we're talking about Word, talking about Excel, and they go get the information they need and deliver it in a much faster way. That's tremendous efficiency for that company. And you were buried by data before, and now you're getting tremendous value from the data now.

And so maybe a couple of examples there for you from Foundry to Copilot Studio about how customers are really engaging with the AI stack in ways that, again, real value to real people and then real efficiency and productivity, which is great for the employees. They toil less to get the job done. And it saves them money, too, in the process. It's a good cost story. So it's a win-win-win. And Microsoft is pretty differentiated when you think about our experience in this space, the stack we have, the relationships we have with leading model makers as well that customers are betting on regularly.

Great. Great. Good. That's super interesting perspective. Maybe we could pivot to the comments that Satya has made around AI and core. They're very interrelated. You almost can't separate the 2. One brings in the other. Would love to get your perspective on how that is. Is AI kind of the tip of the spear for some of these bigger multi-cloud projects you're seeing? And just would love to get your perspective on kind of the interplay between AI and the balance that you're talking about between GPU-based AI and then the traditional CPU-based data center business that you're in.

Yes. It's a really great question. I mean AI is not AI without the cloud, and I'm not just talking about plugging in GPUs here, right? AI is just a part of an application stack, and the application stack sits on the cloud. And that's for any function of what you're doing. You might be in model delivery and you might have an application and you have end users that are querying that app and getting prompts and getting value back or you might be in the process of doing fine-tuning or any other type of Frontier model creation or just using another AI service out of the box. And to do all that, you need the cloud.

And you need compute, you need storage, you need networking. Scott Guthrie recently at our Build event, I think gave a great example about how we're working with OpenAI and what they buy from us in addition to GPUs. When you think about what they're doing, the scale is extremely large. I mean, obviously, the fastest-growing consumer app in history. And so that proposes -- that poses some real challenges when you think about cloud delivery that they have to work through. And what they're using today is way different than what they were using 2 years ago, but they need a scalable data warehouse that will do many petabytes of data with trillions of transactions.

And the service they use to do that is Cosmos DB. That's what they're choosing to do. To do the CPU work that they have to do to do any type of scenario from app serving to agentic scenarios, they need VMs. They have to run VMs to do that. And in fact, they need over 10 million cores to do it. And that's a lot. And so like they chose Azure Kubernetes Service to do that. And it's just -- it's a tremendous, again, evolutionary thing. It was way different a couple of years ago. It's different today, and it's different tomorrow. And the great thing about betting on Microsoft for customers, not just OpenAI. I'm not trying to just name-drop there. This is true for all the customers, but I think the scale matters.

An example is that as their needs change, Microsoft's breadth of capabilities and the services we offer, the geo locations we're in for our data center footprint around the globe mean that they can adapt and grow with Microsoft, and we can continue to deliver a different value as they are at different parts of their journey with AI. And that's one of the reasons why, again, customers are choosing us. And they see us, again, taking the value we learn from Frontier work and then being able to package that up and deliver that value to other customers and then also increasingly use it ourselves.

Right. Wonderful. Speaking of suites, I wanted to ask about Fabric. It's a topic that we hear a lot from the channel and seeing some real traction. I think you cited on the call, 21,000 paid customers, up 80% year-over-year. So real traction there. Can you just take a step back for us, kind of explain what is Fabric and why are you seeing this kind of success in adoption?

Yes, yes. Thanks for the question. I think that the 21,000, 80% number is a fantastic number in and of itself. But I think it's even more impressive when you think about this is the fastest analytics service Microsoft has ever developed. And it's only been GA for 18 months. And so when you think about the growth that it has over that fast of a horizon, there's something special there that we're really proud of. And what CIOs care about, they have a tremendous job. Like in the moment that we're in, to get value, data is your greatest asset.

And your ability to get value of that asset is your ability to mine it, manage it and do all the things you need to do. And you can go get a whole host of point solutions. You can stitch them together, and CIOs can spend time. Instead of being the CIO, they can be chief data integration officers and do data labeling and management and stitching and moving data around or they can come to Microsoft, and they can buy a fully spec-ed out SaaS service that has proprietary Microsoft capability plus any open source solution that you want.

It's got partnerships with Snowflake. It's got partnerships with Databricks. It has all of that. And you can get that on your terms in a very, very seamless and fast way. And it's fully integrated, again, from engineering all the way to visualization with our great Power BI products. And so it's [ hunting ]. The folks who don't want to spend lots of time toiling in the trenches appreciate the time to value that comes from Fabric. And so we've been really pleased with what we're seeing.

Wonderful. Great. Why don't we shift to Microsoft 365, the commercial business? Wanted to get your thoughts on just the balance of growth between subscribers and ASP. What are -- I guess, just qualitatively, we could start, what are some of those key drivers in both of those sides of the growth equation?

Yes, sure, absolutely. So we shared 15% constant currency net revenue growth. We shared a 7% seat number, pretty simple math. You can impute a ARPU growth of 8%. And let me unpack that a little bit. So we have -- in that business, we have over 430 million subscribers is the number we've shared in the past. And so obviously, with that type of penetration, seat growth is going to moderate in the future. We're still doing well in SMB. We're still doing well -- sorry, small and medium business. We're also doing well with our frontline workers.

And so we will continue to see seat growth. But more and more, as you even see in the numbers I just shared, it's an ARPU game. We're delivering new value in E5. More and more customers are adopting it and stepping up to it. And especially when they think about the full breadth of what Microsoft offers in terms of security, there's a good story there. And so we see lots of good step-ups from -- up to the most premium suites that we offer. Copilot is another driver there. We expect to see lots of growth over time from Copilot. And it's early days, but we're doing well there.

And we're going to be continually delivering new value to those suites over time. And like when you think about -- we just recently rolled out Copilot Chat to all of our users to keep -- let them get the value of AI and all the suites from E3 and above is another example of Microsoft delivering new value to the suites over time. And customers will continue to renew. They'll continue to true up and step up, and we think that will drive ARPU over the long run.

Excellent. Excellent. While we're on the topic of Copilot, would love to get your perspective on kind of where we're at in that adoption cycle. Where have you seen success? And going forward, how are you thinking about the incremental attach rate or user for Copilot?

Yes. Yes. Look, we are seeing good traction. We have hundreds of thousands of customers up 3x year-over-year. We are doing larger and larger deal sizes with Copilot every day. And so we see good signals. It's moderate growth. It's pacing well. It's the fastest suite we've ever launched, which is great. And we're increasingly learning how to deliver new value, how to deliver it in ways that customers want and can use easily. I'll use an example of like some new value we've delivered in the suite.

We just launched Researcher and Analyst, and they're fantastic products. And they're products that are deeply integrated and differentiated from Microsoft because they're sitting on top of our graph, and they're sitting on top of the customers' data that's stored within SharePoint and other locations in Microsoft services. And like I -- I use Researcher all the time. I recently had a meeting that -- it was a strategy meeting. It was over a project/product that had gone through many strategic revisions over the years. And we are coming in for yet another strategy review. And I'm going to spend lots of time combing through all my e-mails, all my documents to remind myself of the fact set and what we were thinking at the time we made a different strategy decision a year ago or things like that.

And then I'd spend a bunch of time doing research externally and say, what are my -- what's my competition doing? And so instead I just outsourced it to Researcher. Asked it to pull it together, summarize it, give me some really nice tables and do a comprehensive analysis much deeper than what you get from a deep thinking kind of standard Copilot Chat. Came back minutes later, it was fantastic. I mean it was so simple. I saved so much time doing that. And then I sent it out to all my friends who were going into the same meeting. And then we entered the meeting with shared context, and we were able to get to decision-making faster. And Satya does this all the time.

We have meetings every Friday with an SLT and some of them earlier in the week, too. And we'll regularly come into meetings and Satya will go to start jumping into like a PowerPoint or a Word doc or whatever. And Satya will just blast out on Teams like check out this Researcher I did over the weekend on this topic. And so we all pause and then we read the Researcher and then we come together. And again, we get to the point of having decision-making of reasoning over what we're going to do as a company with that shared understanding and that shared context. And it's just so good. It saves so much time in big meetings that matter, like the ones I'm talking about. And so I'm a big Researcher fan.

That's great. That's great. I guess now that you're in -- it's been over a year, 1.5 years into the Copilot cycle, what have been some of the learnings? You mentioned some learnings that -- what's gone well in kind of the go-to-market and how you positioned it? And what are some of the areas where you think, well, we can -- we're figuring this out, and we're working on driving the adoption rate higher?

Yes. Like years ago, when Satya became the CEO, he really pushed this kind of like growth mindset, learn at all culture and changed within Microsoft the way that we think about learning and growing. And that shows up in everything from the way we work together as employees to the way we engage with customers and the way we build product. And so I think that we were capitalizing on an asset that was built years ago in a time when it matters most, when we're willing to go and put features out, ship those features, let customers use them and then just deeply wallow in the data and what's happening, how delighted our customers, what are they doing with it.

We've changed the way that the prompts show up, the way the boxes show up, the way the shading looks on -- again, we've already shipped it all, but we're in there tinkering. We're trying to figure out exactly how we can take that human diffusion problem with new technology and accelerate it as fast as we possibly can. So we've made thousands and thousands and thousands of changes to the product in real time as we see customers engage with it. And so that's the beauty of what we're doing is we get to take that learning mindset and take it and turn it into a capability that becomes an asset as we go deliver new value over time.

And so we're definitely not done. We'll continue to make changes, but [ we make ] informed changes. We spend so much time every week as a senior leadership team wallowing in the metrics, what is the user data telling us? What are the sessions telling us? And how should we think about tweaking products to give? Again, being very customer-centric and getting the technology in their hands to deliver value is our prime focus. And so we expect -- we have learned a lot. We expect to learn more, and we'll keep iterating our way to it.

Wonderful. Wonderful. Great. Why don't we shift to security? We hear quarter after quarter when we do our calls with some of your partners, that security is one of the #1 use cases that are driving workloads to Azure and across the business. So security is such a broad thing. Microsoft has such a robust stack in security. So maybe just if you could just start with what are -- if you could unpack a little bit, what is the suite and what's driving the success?

Yes. So we've shared that we're like 1.4 million customers and like 900,000 of those are using 4 or more services, and that's up significantly year-on-year. And as we look at like what those customers are doing and you look at that number of services going up, is what we see customers doing is saying, look, I've got a broad security set of threats that I got to manage. And it's in applications. It's all over the place. And they can go buy lots of point solutions or they can consolidate to a provider that has broad breadth of signal, broad capability, and can deliver that in a single pane of glass with AI-infused to allow them to do the work they need to do without taking on additional risk and without any seams between the different providers that they would have if they had point solutions.

And these seams matter. They really do. We can tell you firsthand. And so Microsoft is, I think, well positioned from a consolidation breadth of services and then ultimately, the trust because when you have share, when you have large signal gathering capability across your clouds, whether that's end user applications or platforms, you have a capability to get signal and to manage threats. And we think we're uniquely positioned, and customers agree, and that's why they're choosing us.

Wonderful. Why don't we shift to CapEx? You're on track to spend over $80 billion this year in CapEx, and you've guided to spending more next year. What's informing your decisions and your thinking on investments in CapEx?

Yes. Look, I mean, we spend CapEx based off of demand is the simplest way to say it. And demand is a thing that changes all the time. I mean I wish 3 years ago, I would have known how much I needed 2 years ago because I would have been in a much better position and had a lot more revenue, but I didn't. And so it's a dynamic space right now. We're responding as quickly as we can to the demand signals we have. But when we think about putting the capital down, we have, look, strong signal in Azure, strong signal with our Copilots and our application stack on what customers want. Their bookings we beat the last 5 quarters in a row.

When you look at our RPO balance, it's $315 billion. I mean that's all very strong signal that customers are betting on us. They're betting on us on a multiyear basis. We have strong multiyear contracts that say, hey, we're going to bet on you. And I just think it's really important that you know that we take that really seriously. It's a reputation thing. Customers are saying, we believe in your ability to innovate. We believe in your ability to do business right, to do it in a secure way and to deliver it with high quality.

We're betting our company on your ability to have quality uptime and performance. And we take that super seriously. And so we're very grateful that customers do that. But it's -- again, CapEx is really based upon that signal. And we are mindful that things change all the time, and there'll be adjustments up and down, slight tweaks here and there. And we're being -- managing the risk of that capital as well. But it's really demand based, and that's how we think about it.

Sure. Great. As CFO of an organization that is as broad as commercial, how are you thinking about margin across the broader organization? What are some of the puts and takes on how you're thinking about margin going forward?

Yes. We're managing margin very tightly. It's something we have a legacy doing over time within the commercial cloud gross margin that we report out on a regular basis. If you go put a historical plot, you'll see it being managed super well. And -- so we're taking all the learnings and what we did to manage that over many, many years and applying them to AI just like we did before. And to give you a sense of what that looks like day-to-day, each product we have that we go to market with has a target margin that it's eventually going to get to. We build that out deeply with the engineering teams and with the product teams, and we build multiyear, multi-semester plans to go drive down the way that those applications and services utilize the underlying hardware and software of the platform.

And so that's one layer of the stack. Then you go and click deeper and you say, okay, great, we have platform teams that are driving utilization of how you bin pack or how you drive up utilization of a server. And we have -- those teams have targets and efficiency plans as well. And then you go into something like how do you adopt the latest silicon, how do you do innovative things. Like, for instance, we have a great -- we talked about Overlake. We have a network card that is allowing us to offload CPU and drive up utilization significantly. We're building our own silicon that we've shared previously.

And so at every layer of this stack, we're after efficiency. We're after unit cost. Satya talked about all the time. He talked about it in his script again in his last earnings call about being world-class at driving efficiency from our capital deployments. And we, again, [ manage ] every layer of the stack directly with teams. And then the last thing I would say that we do that I'm quite proud of is we create financial incentives to drive user behavior.

And so like if it's better for us, for a team to build to a certain region or a certain type of a VM because we have the ability to do better bin packing or better utilization, we drive financial incentives inside the company to get them to do that. And so that multifaceted approach we've built over time is being applied to AI. And Satya talked about the cost per token going down by more than half. He talked about our power utilization on GPUs being better by 30%. And -- again, this is just the output of all the same thing we've been doing for years in the cloud, applied to the new most expensive scarce resource we have in GPUs.

Makes sense. And while we're on that topic, since you mentioned AI, would love to get your perspective on those workloads and whether you see the path to those getting to the same gross margin level as core cloud in Azure [indiscernible] the puts and takes...

Yes, it's super early days. I mean we had quite a journey on the cloud side originally. We're going to be going on a journey here. But I would just say, I believe we can deliver significant margins there. And there's good tailwinds, I think, that are moving in our favor. And I'm not just talking about GPUs. Satya talked a lot about in his Build talk, if you haven't seen it, I encourage everybody to go look at it. Talked a lot about how much of the benefit, the tailwind in terms of utilization and efficiency is coming from models, is coming from post training, is coming from the way that those application stacks are utilizing the GPU.

And in many cases, saying, hey, what can we offload, what can we parallelize into a CPU or another part of the stack. And there's tremendous scaling laws on that side. Beyond just scaling laws, you might look at in terms of model capability, size of training runs, total token sizes and parameter sizes and things of that nature and hardware. There's a lot of capabilities [indiscernible]. So I think there's good secular trends behind us, along with our experience, are reasons to believe that we'll continue to make progress on that.

But it will take time, and we'll work through it. And we're not -- so we're not predicting anything. We're not sharing anything in detail on that. But I think that there's good foundational elements of why you should believe that we'll continue to chip away at this over time, like we've done in past cloud endeavors and cloud investments.

Wonderful. One more before I ask the final question, but I wanted to go back to the AI adoption question that we were talking about earlier within Azure. You cited a health care and a financial use case. Are there any industries or segments of the business where you're seeing that uptake higher for some of the AI services within Azure? How would you distinguish between just the business overall as to how the adoption cycle is tracking?

Yes, sure. I mean I think -- again, I'm not trying to just zoom out every time, but like it really is broad-based. I mean I think customers -- our customers understand the value AI can deliver. So they're all -- many, many, most are trying to figure out how exactly they're going to do it. And -- so they're just at different parts of the journey. Some are very sophisticated and they're way down the path of having taken an open source model and completely customized it and done all the post-training work they want to do, and they're doing very sophisticated things with delivery.

And some customers are just taking a straight API, and they're trying to figure out how to use that in a way that makes sense for their product. And we're -- again, we have that great breadth. We're with every customer, no matter where they're at, with them on the journey, helping them figure it out. As we have figured it out ourselves, we can then take that learning and share it with them. And so it's a great journey. And it's, again, very broad-based. And it's naturally very diverse because every customer is in a different place in terms of sophistication and modernization.

Makes sense. That makes sense, Matt. Well, maybe just to wrap it up, is there anything that we haven't talked about that you think is important to touch on here?

Yes. No, it's a great question. I would say, for me, I'll say one that maybe you didn't expect. I think the pace, the pace of it all right now is very noteworthy. It's easy to miss. I mean we've shared at Build conferences and other various things Microsoft has done publicly. We've shown dot plots of number of shipped products and number of shipped features, and it just is this massive curve up and to the right. And it just continues. I mean, Microsoft is more aligned moving at a faster pace than ever before. And that takes real leadership, and that takes real determination.

And in many cases, it takes real stamina because I can tell you, personally, I feel like I'm just constantly sprinting every day, every week, night and day to go keep pace with what our customers want to do, keep pace with the technology and deliver it. And then we talked about the learning earlier and tweaking and adjusting. And it feels very vigorous inside of Microsoft. It's challenging, but it's so exciting.

And like for me, having been at the company for 19 years, the energy is fantastic. And I think we're doing a great job executing at pace. And that's not easy to do when you're Microsoft scale. And I think that's something that we're really proud of and that I feel personally, I get the benefit from. I feel very blessed to be part of it.

Absolutely. We can see in the results. And this has been great, Matt. Thank you so much for joining us. Really enjoyed the conversation.

Yes. I appreciate it. Thank you.

Thanks.

Microsoft has a very special place in my heart. I was a Microsoft developer in the 90s. And so I said to Takeshi, I wouldn't be here without Microsoft. So I am a little biased because it helped me get up to the main streets in Minnesota. Takeshi, welcome, 27 years at Microsoft. I know you said that there are others that have beat you in terms of being at Microsoft longer, but 27 years, just run us through what you've seen over those 3 decades and maybe lead us into your current role and what you're doing now.

Yes, I'm a bit of an oddball at Microsoft. I'm native Japanese. I grew up in Japan, and worked for the Japanese government as an MITI during trade negotiations before joining Microsoft in the engineering group, of -- what was then Windows NT in 1997. And then I [split] over to marketing in 2003, working on sort of Office as a traditional shrink-wrapped software back then and then worked on sort of transforming that business into a cloud business, starting with Office 365. And then it’s a lot of it. It’s about the cloud and I worked on Azure. I became the commercial CFO a few years ago and then the CMO last October -- no, a year before October. So it's been just over 1.5 years maybe. And they spent a lot of time on cloud, AI and growth.

We'd like to talk about cloud and AI. Let's talk about AI. A few years ago, you really started to push before anyone. You guys got out in front. You led the charge. It's now been a couple of years in. Maybe just bring us up to speed where you're at? What are the next steps? How do we think about -- I know you unveiled a lot of Copilots to build, and we're getting new reasoning models, where Jefferies is very excited. We're going to roll out Copilots to 90% of our firm. We're 6,000 employees. We're tiny, relative to other financial services firms, but we're going to 90%. We're at 10% today. So we're a huge believer.

Well, I mean it's one of those things where you're never satisfied. Certainly, I'm facing a room full of people who is never satisfied with what we do. We need to do better. That's great. And our CFO constantly reminds us of that. Our CEO reminds us of that. I get love letters of that ilk very often. But at the same time...

Is that true that there's a room called the -- what's the room that Amy called the biggest room for improvement? Biggest room in the house is the room for improvement, yes.

But at the same time, having worked on multiple transitions, first, actually from sort of a software license sales back in the office shrink-wrapped days to moving to an annuity sales model and then to a cloud subscription model with the Office 365 and then adding a consumption business model in Azure. Like, having watched these and participating in these transitions over the multiple times with Microsoft, the thing I can say about sort of what's happening in AI is just the speed is so different. It's crazy to think that we talked about Copilot, like it's a very well-established thing, but we only made the product generally available in November of '23. So the whole product has been in market generally available just for 1.5 years plus.

And so the pace of both engineering throughput and then sort of the resulting go-to-market adjustments have been high. And unlike some of the transitions before, like the cloud, where we had the sort of the opportunity to fast follow a bit because there were precedents set before us that we can learn from. We're now in a place where we are leading and we often make decisions that have no precedents before. So we have to make -- be the first one to make a lot of decisions and set the pattern and lead the industry and sort of our customer engagement scenarios and invent new ways to do that.

So that's really exciting. But the other thing I can say is the AI work really builds on sort of the strong foundation we built in the cloud. When we were working initially in the cloud transition from on-prem software, we really didn't have a base of cloud, so we have to build that up from basically scratch. Whereas now we have a base of cloud and AI sort of essentially layers on top of it in a nice way, given the fungible infrastructure we have. And so that gives us a lot of scale fairly immediately.

There's a -- I think the industry is trying to grapple with how you price AI. Some have said it's foundational. Some have said it's a Copilot, some have said it's by the work done. There's different ways to do it. How do you think about how customers should pay for AI at Microsoft?

I don't think it's one size fits all, and there are different constituents. For example, developers certainly would like to consume things in a very consumptive way, whether it's in the form of an API or tokens. And so we have that and that's doing really well on the Azure side with the variety of AI services we offer and the application platform. We think of -- as what we call AI Foundry as effectively an app server for developers to be able to build applications quickly and efficiently and manage it.

But on the SaaS side, like Copilot, there are subscriptions that we have and we also are giving customers options to basically say, "Hey, you have a -- sort of a population in your employee base for whom paying for a subscription makes total sense." But then you have all sorts of frontline workers or broader constituents that are in your enterprise that you still like to provide AI capabilities in their daily lives for, but not necessarily ready to buy a sort of a monthly subscription for every one of them. So we sort of opened up new options with business models like Copilot Chat that essentially enables customers to consume even sort of SaaS AI services on a consumptive business model. So we're sort of giving choices across the board, and we're continuing to learn.

Last week at Build, you had a lot of announcements. Maybe what was most important to you that you unveiled last week?

Well, Build is our main developer conference, so the entire context is very, very focused on developers. So we had our CTO, Kevin Scott, talk about sort of the notion of an agentic web and how we're embracing open protocols like MCP and A2A to be able to enable scenarios where agents can work across individuals, teams and organizations to unlock new scenarios. So this notion of new ecosystem and embracing open standard was a key part of that story.

But given that it is a developer conference, it was a great opportunity for us to talk about how we are reimagining the software development life cycle. It's actually interesting to remember that the name Copilot came from our first Copilot product, which was GitHub Copilot. GitHub Copilot was our first Copilot product. And it has started with things like code completion. So it's interactive. You're the developer and you're writing half the line and then the remaining line code completion, the AI helps you.

But then it's evolved since to be able to do things like multi-file edits and from being a sort of a payer program to now more of a peer programmer where you can assign issues and essentially sort of treat AI as sort of an autonomous thing that can complete tasks on their own. And so the Copilot -- GitHub Copilot has been a great showcase in how the capabilities has evolved over time, and we sort of talked to developers about how that really changes the workflow and make them more productive.

And we had announcements like Visual Studio extensions to GitHub Copilot being open sourced, which really generated a lot of excitement amongst developers to be able to sort of use GitHub Copilot and VS Code in more flexible ways.

And then I think for us, because we have such an enterprise footprint, we spend a lot of time talking about how we are delivering this ability for developers to build AI applications in a way that meshes with the governance and security and compliance structure that enterprises have. So how does an agent application that gets built on Azure intrinsically support the identity framework that we have in Entra? How does the -- sort of all the logging capabilities that you can use in AI Foundry as you build AI -- as you build your AI applications can compose with our governance, data governance solutions like Purview or integrate with security in terms of our Defender products. And so we sort of paved the -- articulated our point of view on how AI development can be done in a way that's secure and manageable in a way that fits with the enterprise estate.

You get the magic wand and you can wave it over in enterprise to get AI adoption going quicker. I think maybe it's been maybe slower than maybe some of us all thought it would be. What are the -- what do you wave this over? Is it data governance, security in your data estate, right? What do you think the magic wand?

Well, I mean, if it were that simple where you have just one silver bullet, you would be just working on that one silver bullet. It is a net new asset for enterprises to manage, right? It's not like, hey, I used to have this thing. I know a database, I'm replacing a database, upgrading a database, migrating something better way. Those are all relatively known things. In many aspects, AI is new for companies to adopt. So it's not just about the product. It's about sort of the policies that they now have to think through. What are the business workflows within the company, organizational structures, skill set.

So all those sort of things that are actually not necessarily the tech, but more about skilling, more about organizations, more about how people within the customers collaborate in different ways. That is the typical enterprise adoption cycle for net new technologies versus linearly sort of upgrading things you already know. So that accelerating, that's of course, the biggest lever, frankly, beyond any particular given tech per se.

On the last earnings call, Amy pointed out that strength in core drove a lot of the upside. Everyone kind of looks around and says like what's happening in the core to accelerate growth now at Azure?

Well, the -- I think for financial communities, we talk about AI and non-AI somewhat separately. That is not how we engage with a customer. The customer conversation isn't like let's have an AI conversation and a core conversation. That's not how we do go-to-market because every migration project that from a financial community standpoint might seem core is already always connected with what are we going to do once we migrate and modernize and we want to use. We're doing this because we want to be able to apply AI on top.

So for example, just as an example, we talked about AI Foundry with a great customer momentum with 70,000 customers. But -- and that counts in sort of the AI growth. But Fabric, which is what our data platform solution is growing basically 80% year-over-year. And I think the customer usage of OneLake, which is where you would store your data for Fabric, I think it is growing like 6x year-over-year. And that's all in furtherance of customers getting ready to basically apply AI solutions on top of the data estate they have, but that counts as core. But that's the same customer engagement in the same meeting with the customer, we're talking about AI Foundry and Fabric together. And so we need to get this balance right in terms of driving growth on both sides. And certainly, Q2, when Amy calls out execution issues, we don't take this lightly. And the Shockwave went through the entirety of the company.

I can imagine.

We heard it, and we sort of worked hard.

Yes. It was interesting because I had a call back with Amazon post third quarter, like what are we going to stay off of that Microsoft print, they blew it out. Even Amazon was like, wow, like what -- like I know there are multiple things that went right, but what's at a high level going really well for Azure right now?

Well, right now, I think it's -- the execution forecast has really helped us, particularly amongst our -- what we would call sort of the large enterprise customers, maybe the top 500 customers that were balancing the conversations about net new projects in AI that might actually consume a lot of engagement cycles, but not necessarily immediate consumption versus migration projects that might actually not consume as many sort of customer engagement and meeting minutes, but actually drives lots of consumption.

How do we actually balance the execution in the face of the customer to make sure we're having the right conversation on the AI side, but also driving things that can drive consumption sort of we're getting that balance, I think, better calibrated. Also our scale motion, sort of the smaller enterprises and mid-market and below, we made some internal changes that's still sort of working through the system, and that's starting to show improvement, although we have more to do there, too.

Macro has been on everyone's mind. Maybe you don't hear it as much as we do in our community, but it doesn't seem like it's having any issue or any big concern in your execution.

Well, of course, no one is immune from macro. But for us, as far as we talked about in our Q3 results, too, we haven't seen a lot of impact from that. And the good news is, I think the customer spend in this area, digital transformation, cloud migration, security, those are some of the most defensible spends, I think, that customers would want to prioritize. And so I think we're in a good spot. Not to say we're immune, but we feel pretty good right now.

My only criticism is we got to get Copilot to tell better jokes. Why did Microsoft employees bring a ladder to work? Because they heard the company was going to the cloud. That was from Copilot. We're going to work on.

I'll tell the...

Okay. Sorry. There's just a side injection there. I know there are questions from the audience. If you guys have a question, you can raise your hand, you can jump in. Any big open questions for Microsoft?

[Technical Difficulty]

If I understood the question correctly, the question was about how we think about data center capacity for Azure?

[Technical Difficulty]

And the investment on capital? Like I would say we're basically building to demand. We're very sensitive to demand signals. And so we want to make sure that we're basically building to meet the demands that we see. And so that's how we think about it.

[Technical Difficulty] there's obviously anytime you guys pull back or adjust those plans [Technical Difficulty] how nimble can you be with those CapEx plans [Technical Difficulty] when there are changes in demand?

Well, I wouldn't claim to be a super expert on it. But basically, there are long-term assets, right? A lot of the CapEx spend is for assets that are going to be sort of utilized over a long period of time, 10, 15 years. And so some of those are somewhat lumpy and there's not a lot of near-term variability you have. But then the kit that you installed in these data centers, the server racks, the CPUs and the GPUs that we install can be as close to the demand signals as we can. But then the investment from an aggregate volume standpoint encompasses both those kind of server and kits versus the long-term investment is kind of a blended number. And so that's all I can say.

I know you love all your Copilots equal, but is there one category where you're excited maybe usage isn't as high today and you think it could explode? Maybe one thing has come up maybe in security that was a little later in terms of getting that out, is that, could it be security, could be another category? What are you most excited about in that family?

Well, for me, the exciting thing about Copilot is the fact that it actually gives us an opportunity to reinvigorate every one of our franchises because a little bit like the Internet, AI and Copilot type use cases aren't unique to one category. We basically get to reinvent productivity with Microsoft 365 Copilot. We get to reinvent security with Security Copilot. We get to reinvent coding and productivity for developers with GitHub Copilot. So for us, Copilot isn't one thing, it's actually something that actually can reinvigorate all of our product franchises, inclusive of all of our major franchises, Windows, Microsoft 365, all the others. So that's what's exciting to me as a CMO of the company.

Other questions?

[Technical Difficulty]

Well, I would say the AI adoption by customers, of course, varies for each customer. But I would say there's definitely be -- a distinct phase shift where it's not about POCs. We're really seeing large-scale deployments, particularly when it's targeted against known business processes. So it really boils down to not sort of an ROI in the broader sense, but against a given KPI.

Let's talk about customer service or let's talk about HR processes, or let's talk about essentially, very operational things like, invoice reconciliation or those kind of things and where customers are saying, "Hey, I can target this. I have a known KPI and I can develop and deploy a solution against it and get -- see returns." And that creates the next project, the next project cycle where it's way more than a POC and much more into production deployments is what we're seeing.

The whole concept of data governance is a big theme for getting ready for AI. What are you doing to help companies get ready on that side? Maybe talk through the products, the strategy, and the adoption of.

Well, actually, our data governance offering is called Purview. And certainly, if I think about even the search traffic volumes that we get on customers searching for Purview, it's actually dramatically increasing. And so the customer interest is not just AI, but how do I govern data associated with the introduction of AI technologies like Copilot is very, very high. And so I would say interest in products like Purview is very high.

The other thing I would say is we have been quite responsive to use the customer engagement and the feedback we get to actually add some basic data governance capability into offerings like Microsoft 365 Copilot in the base. So you don't have to buy another product. Like as an example, Copilot essentially only accesses things that you can access. But then oftentimes in an enterprise, there are lots of things that anybody can access, but you didn't know that everybody could access, but Copilot would efficiently find them.

So we've actually enabled scenarios like Advanced Access Analytics so that you can say, hey, who can access this file and how do you govern it and actually making that part of the Copilot offering so that IT, doesn't have to buy another product as they sort of go deploy Copilot.

So those are changes we made in the last 12 months based on customer feedback. So we're sort of improving the base, what you get with Copilot, but also having a sort of a Purview solution that can really look at your data state across the board, and then that's having a lot of customer interest.

Our CIO of Jefferies went to a Microsoft briefing and he came back and I didn't think he was going to say this, but he said, the one thing I was like blown away by was Power Apps and what we can do in an age of AI to kind of create custom apps for Jefferies that are -- that's different for us than others. And maybe if you can just expand on Power Apps, what it means. Some may -- not understand what it is, but what, yes, explain.

Yes. Well, basically, like we often talk about it as a sort of low-code/no-code solution, and it might mean very little to people in this room. But the way I think about it is, if you think back in the early days of productivity, almost early days of Office, document creation used to be a back-office operation and if you have to respond to an RFP, there was a back-office operation process to be able to respond and have a full faith and full-throated response to an RFP. But then with the Office and Office productivity applications becoming ubiquitous, doing things like RFP responses or doing financial analysis become much more of a frontline operation.

Power Apps basically is doing that, exactly the same for app creation, just like you can create a document. Basically, it doesn't have to become an IT project for something to -- for somebody to build an app and essentially fill that last mile of application needs, because otherwise, the IT backlog per application is becoming longer, not shorter. So being able to empower the frontline office people to be able to essentially empower themselves, with applications they can create themselves is what Power Apps is doing, and it's growing very, very rapidly.

So do you envision, you sit in front of the -- your system and you say, I'd like this customer [training] app built and here are the characteristics, you input it and it builds the application for you. Do you envision a world where...

Yes, yes. I mean, like if you actually look at some of our build demos with Power Apps, it's doing exactly something like that. You literally tell Power Apps what kind of apps you want and Power Apps will help you build it and actually create a mobile app and a web app that you can then deploy or share just like a document. I [would make] one, and I'll share it with you in a sort of a secure compliant way.

One question on the back.

[Technical Difficulty]

I can't speak to what we expect on the sort of the device sales impact, but we're certainly taking advantage of the fact that we have a deep history of innovation on not just the cloud, but software that you can deploy to on-premises and edge devices. And so one of the announces we made actually at Build was this notion of Foundry Local. So then you basically can train your models in Azure, let's say, with that AI Foundry, and you can deploy it as a container to different local devices. And then we also announced Windows AI Foundry, which is essentially a way to have a layer in your Windows device that can actually let developers build applications really quickly using local AI. So we are definitely looking to continue to invest in this notion of AI, both in the cloud, but on the edge and having those 2 work really well together.

One more question over here.

[Technical Difficulty]

Well, the competition, I think, at AWS or anybody else, I think, happens in a very project-by-project basis, I think. So whether you can talk about data, you can talk about infra. But for us, I think the -- our strength really lies in our ability to serve the enterprise needs holistically. It's across all the stack from infra to data, all the way to SaaS layers that integrates with everything we offer with Microsoft 365 and Dynamics 365.

Great. Thank you so much for joining. Appreciate your time.