Google just dropped a major signal about 2026: expect a "significant increase" in capital spending as the tech giant doubles down on AI infrastructure. The announcement came as Alphabet reported its first-ever $100 billion quarterly revenue, beating Wall Street expectations and revealing a $155 billion cloud backlog that's driving unprecedented investment demands.
Google just sent a clear message to Wall Street: the AI infrastructure arms race is about to get a lot more expensive. The search giant's executives delivered the news Wednesday alongside blockbuster quarterly results that showed exactly why they're preparing to spend big. Alphabet reported its first $100 billion revenue quarter while announcing plans for a "significant increase" in capital expenditures for 2026, adding fuel to an already red-hot AI investment cycle. The company's current 2025 spending range of $91-93 billion - already increased twice this year - apparently won't be enough to meet surging demand from enterprise customers hungry for AI compute power. "Looking out to 2026, we expect a significant increase in CapEx and will provide more detail on our fourth quarter earnings call," Chief Financial Officer Anat Ashkenazi told investors during the earnings call. That spending trajectory puts Google squarely in the center of a massive infrastructure buildout that's reshaping the entire tech landscape. The numbers behind Google's spending spree tell the story of an industry transformation happening in real-time. Google Cloud's backlog hit $155 billion in the third quarter, with CEO Sundar Pichai revealing the company secured more billion-dollar deals in the past nine months than it managed in the previous two years combined. Cloud revenue jumped 32% year-over-year, keeping pace with rivals like Microsoft and Amazon in the race to capture enterprise AI workloads. The scale of these deals is staggering. In August, Google landed a $10 billion cloud contract with Meta spanning six years. Just last week, AI startup Anthropic announced a partnership giving it access to up to 1 million of Google's custom Tensor Processing Units - a deal worth tens of billions according to industry estimates. These massive commitments explain why Google has already raised its 2025 CapEx guidance twice, jumping from an initial $75 billion to the current $91-93 billion range. Most of that money flows directly into data centers and the specialized chips needed to train and run AI models. Google isn't alone in this spending frenzy. also raised its 2025 capital expenditure guidance Wednesday, bumping the low end up by $4 billion to a new range of $70-72 billion. The synchronized increases across Big Tech signal just how urgent companies view the AI infrastructure race - and how willing they are to spend whatever it takes to stay competitive. The investment strategy appears to be working for Google's own AI products too. The company's Gemini AI assistant now boasts over 650 million monthly active users, up from 450 million just last quarter. Meanwhile, AI Mode within Google Search has reached 75 million daily users in the US, with query volume doubling during the third quarter. That growth helps explain why Google's core search revenue climbed 15% to $56.56 billion, easing concerns that AI chatbots might cannibalize the company's advertising goldmine. Instead, Google seems to be successfully integrating AI features that enhance rather than replace traditional search, with executives confirming they're testing ads within AI Mode. The spending commitments also reflect the brutal realities of the AI infrastructure market. Training frontier models requires enormous amounts of specialized compute power, while serving those models to millions of users demands massive server farms packed with cutting-edge chips. Companies that fall behind in this buildout risk losing access to the most advanced AI capabilities - and the customers who depend on them. For investors, Google's 2026 spending preview offers both opportunity and uncertainty. The company's willingness to invest heavily suggests confidence in long-term AI demand, but it also means lower near-term profit margins as billions flow into data centers and hardware. The key question becomes whether Google can convert that infrastructure investment into sustainable revenue growth fast enough to justify the enormous capital outlays.
