Analyst(s): Nick Patience
Publication Date: February 6, 2026
Alphabet’s fourth quarter 2025 underscored AI as a growth vector across Cloud, Search, and subscriptions, aided by expanding Gemini usage and an expanding enterprise pipeline. Management emphasized continued supply constraints and a rigorous investment framework as FY 2026 capex ramps to expand capacity and efficiency.
What is Covered in this Article:
- Alphabet’s Q4 FY 2025 financial results
- AI infrastructure and capex strategy
- Google Cloud performance and enterprise AI adoption
- Search, Gemini, and ads monetization
- Guidance and Final Thoughts
The News: Alphabet (NASDAQ: GOOGL) reported Q4 FY 2025 revenue of $113.8 billion, up 18% year over year (YoY), versus Wall Street consensus of $111.4 billion. Google Services revenue was $95.9 billion (+14% YoY), including Google Search & other at $63.1 billion (+17% YoY), YouTube ads at $11.4 billion (+9% YoY), and Google subscriptions, platforms, and devices at $13.6 billion (+17% YoY). Google Cloud revenue was $17.7 billion (+48% YoY). Operating income was $35.9 billion (+16% YoY), with operating margin at 32.0% (flat YoY). Net income was $34.5 billion (+30% YoY) and diluted EPS was $2.82 (+31% YoY).
“It was a tremendous quarter for Alphabet, and annual revenues exceeded $400 billion for the first time. The launch of Gemini 3 was a major milestone. Search saw more usage than ever before, with AI continuing to drive an expansionary moment,” said Sundar Pichai, CEO of Alphabet.
Alphabet Q4 FY 2025 Highlights Cloud Acceleration and Enterprise AI Momentum
Analyst Take: Alphabet’s Q4 FY 2025 results show AI translating into commercial traction across Cloud, Search, YouTube, and subscriptions while the company scales capacity for sustained demand. Google Cloud’s 48% revenue growth and margin expansion to 30% demonstrate that AI infrastructure investments are generating returns, not just consuming capital. Management signaled continued supply constraints through 2026, a disciplined capex framework, and an AI-first operating model that is already improving engineering productivity. With Gemini adoption expanding to 750 million consumer monthly active users (MAUs) and enterprise API throughput exceeding 10 billion tokens per minute, Google is positioning its infrastructure and model portfolio as core differentiators. The setup for FY 2026 centers on capex execution, platform integration of Gemini, and monetization of AI-native experiences across Cloud and consumer surfaces.
AI Infrastructure and Capex Strategy
Alphabet outlined 2026 capital expenditures of $175 billion to $185 billion, almost double the 2025 spend, reflecting long-horizon investments in technical infrastructure to meet persistent demand while navigating power, land, and supply-chain constraints. Management noted approximately 60% of FY 2025 investments went to servers and roughly 40% to data centers and networking equipment, with a similar mix expected in FY 2026. The company emphasized a rigorous, multi-year investment framework that prioritizes both near-term and long-term growth, funded by ongoing efficiency gains. Internally, AI agents now generate about 50% of Google’s code, and the company continues to construct most of its own data centers to optimize for workload efficiency. Management expects to remain supply-constrained in FY 2026 as it ramps capacity to support DeepMind, Cloud, and Services. For enterprise customers, this constraint environment raises questions about lead times and capacity allocation—factors that may influence multi-cloud hedging behavior among large buyers. The strategic takeaway is that capex intensity reflects a commitment to lead in AI infrastructure while driving operational leverage over time.
Google Cloud Performance and Enterprise AI Adoption
Google Cloud posted 48% YoY revenue growth in Q4 FY 2025 to $17.7 billion, driven by enterprise AI Infrastructure, enterprise AI Solutions, and core GCP products. Operating income more than doubled year-over-year to approximately $5.3 billion, with operating margin expanding to just over 30% – a significant profitability milestone that addresses investor concerns about AI infrastructure ROI. Management highlighted strong demand translating into a cloud backlog that more than doubled to $240 billion. The composition of this backlog warrants attention: management noted that deal sizes exceeding $1 billion are becoming more common, suggesting enterprise commitments are scaling with AI adoption. The end-of-2025 annual run rate exceeded $70 billion.
Gemini serving unit costs declined 78% over 2025 through model optimizations, efficiency improvement, and utilization gains. This cost trajectory is directly relevant to enterprise deployment economics and positions GCP competitively on inference workloads, where cost-per-token increasingly drives platform selection.
More than half of Alphabet’s machine learning compute is expected to support the Cloud business in FY 2026, underpinning momentum for training, inference, and agentic workloads. TPU availability, end-to-end data center efficiencies, and a broad accelerator choice were positioned as competitive differentiators for GCP. The investment thesis centers on AI workload mix expansion, deeper model/API integration, and rising enterprise adoption. Forward demand indicators – particularly the backlog growth and supply-constrained environment – suggest sustained Cloud momentum into FY 2026, though revenue recognition timing and deal ramp curves will influence quarterly cadence.
Search, Gemini, and Ads Monetization
Management reported no evidence of Search cannibalization from the Gemini app; instead, AI Overviews and AI Mode are expanding overall query volume, including commercial queries. Gemini-based improvements in Search ads are enhancing intent understanding and unlocking longer, more complex queries that were previously harder to monetize, with AI Max adoption by hundreds of thousands of advertisers. The Gemini app reached over 750 million MAUs in Q4 with rising engagement and retention. In YouTube, ads growth was driven by direct response, while subscription momentum (Music and Premium) continued; management framed subscription shifts as net-positive to the overall business. Shorts averaged 200 billion daily views, and Google’s focus on CTV, shoppable formats, and creator-brand tools aims to further integrate performance and brand outcomes. The implication is that AI-native experiences across Search and YouTube are expanding monetizable surface area rather than substituting it.
Guidance and Final Thoughts
Alphabet guided 2026 capital expenditures to $175 billion–$185 billion as it scales compute capacity to meet AI demand, with a continued focus on efficiency, disciplined allocation, and healthy balance sheet management. Management expects supply constraints to persist through 2026, informing earlier, larger, and more diversified infrastructure planning. The company maintained its quarterly dividend at 21 cents per share while continuing to invest in growth and platform capabilities. With Gemini integrated across products and APIs, and with Cloud forward demand indicators strengthening, execution on data center buildout and AI monetization is the critical watch point. The forward view hinges on balancing capacity expansion with product-led growth in Cloud, Search, YouTube, and sustained enterprise adoption
See the full press release on Alphabet’s Q4 FY 2025 financial results on the company website.
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Author Information
Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.
