$555B of Cloud Spend

Microsoft & Google both announced earnings yesterday, & the scale of AI adoption remains staggering. The infrastructure businesses are growing at accelerating growth rates that are the envy of businesses one-hundredth the size.

Capex

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…. 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. - Microsoft

We now expect CapEx to be in the range of $91 billion to $93 billion in 2025, up from our previous estimate of $85 billion. - Google

Both companies are increasing their data center buildouts as a result of demand. We could see $100B run rate on data centers in the next 12 months for each of these companies, up 33% from their projections at the beginning of the year.

RPO

From Microsoft our commercial RPO increased over 50% to nearly $400 billion with a weighted average duration of only 2 years.

Google Cloud’s backlog increased 46% sequentially and 82% year-over-year, reaching $155 billion at the end of the third quarter.

Remaining performance obligations, or RPO, called backlog by Google, represent commitments customers make to spend dollars on infrastructure. Both metrics are experiencing tremendous growth, which drives predictability in demand & ultimately confidence in capital expenditure investments, now totaling $555 billion across these two companies.

Efficiency & Small Models

We increased the token throughput for GPT-4.1 and GPT-5, two of the most widely used models by over 30% per GPU.

Our Phi family of SLMs, which now have been downloaded over 60 million times, up 3x year-over-year.

Earlier this year, Microsoft announced that they were generating 90% more tokens per GPU. This increase appears to be concentrated in smaller or non-thinking models, given the statistics around GPT-4.1 & GPT-5.

User Adoption

We now have 900 million monthly active users of our AI features across our products. - Microsoft

The Gemini app now has over 650 million monthly active users, and queries increased by 3x from Q2.

With OpenAI at close to a billion monthly active users, Microsoft & Google are not far behind. These figures are not deduplicated, so many users likely overlap. Nevertheless, the broader adoption of AI, especially by consumers, is incontrovertible.

Developers

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.

And more than 13 million developers have built with our generative models.

In my market sizing for developer tools, I used to estimate the total developer market to be around twenty-seven million developers. I now see that number approximates two hundred million, or about 3% of the global population, which is staggering.

$100M & $1B Deals

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.

Cloud has signed more billion-dollar deals in the first 9 months of 2025 than in the past 2 years combined.

Despite all the concerns about circular financings & over exuberance in the ecosystem, the financial results are incredibly compelling.

Cash Position vs Annualized Capex

Both companies are spending at or above their cash positions on AI infrastructure. Microsoft is committing 137% of its cash reserves to annualized capex, while Google’s capex represents 93% of its cash position.

Despite healthy net cash positions ($66.6B for Microsoft, $76.9B for Google), these aggressive investment levels signal extraordinary confidence in AI demand & may require additional debt to finance the AI boom.