Google is the only major hyperscaler that doesn't pay Nvidia's margins for most of its AI compute, because it designs its own chips, in-house, all the way down.
Who they are
Google, the search engine that became the core of parent company Alphabet Inc., is one of four hyperscalers spending the most on AI infrastructure worldwide. What sets it apart structurally is vertical integration: unlike AWS and Azure, which primarily resell Nvidia-based compute, Google designs its own AI accelerators, called Tensor Processing Units (TPUs), and runs its own Gemini model family on top of them. CEO Sundar Pichai leads both Alphabet and Google.
What they do
Google Cloud rents out compute and AI models to outside customers, while Gemini serves consumers and enterprises directly, and the two businesses reinforce each other. At its Cloud Next 2026 conference in April, Google unveiled its 8th-generation TPUs, split into a training chip (TPU 8t) and an inference chip (TPU 8i) that links 1,152 chips into a single pod and delivers 80% better performance per dollar than the prior generation. Gemini is now processing more than 16 billion tokens per minute through direct customer API use, up 60% from the prior quarter.
How Google makes money
Google Cloud revenue hit $20.0 billion in the first quarter of 2026, up 63% year over year, the fastest growth of any major hyperscaler cloud business that quarter, and its backlog of signed-but-unfulfilled contracts nearly doubled quarter over quarter to more than $460 billion. Alphabet's total annual revenue exceeded $400 billion for the first time. CEO Sundar Pichai told investors plainly that Google is "compute constrained" and that cloud revenue "would have been higher" if it could build data centers fast enough to meet demand.
The bigger trend
Alphabet raised its 2026 capital-expenditure guidance to $180–190 billion, more than double the $91.4 billion it spent in 2025, funded partly by an $80 billion equity raise. Because Google owns both the Gemini models and the TPU chips they run on, it avoids paying Nvidia's hardware margins on a large share of its AI compute, a structural cost advantage AWS and Azure don't have to the same degree.
Gemini Robotics: the model layer for humanoid robots
Google DeepMind, Alphabet's AI research lab, takes a hardware-agnostic approach to robotics: rather than building its own robot, it builds the foundation model that other companies' robots run on. Gemini Robotics, a family of AI models built on the core Gemini architecture, launched in 2026 as the "brain" behind Hyundai-owned Boston Dynamics' Atlas humanoid (a partnership announced at CES 2026) and its quadruped Spot, and was separately licensed to German industrial robotics firm Agile Robots, which has already installed over 20,000 robotics solutions worldwide.
Alphabet doesn't disclose robotics revenue separately — any related investment sits inside the Other Bets segment alongside Waymo, which posted a $1.8 billion operating loss in Q2 2026, driven mostly by Waymo rather than robotics specifically. Google has also taken delivery of rival firm Apptronik's Apollo humanoid units for internal testing, making Alphabet simultaneously a model supplier to Boston Dynamics and a customer trialing a competing hardware platform.
Whether the new TPU 8t/8i generation ships fast enough to relieve the "compute constrained" bottleneck Pichai described; how much of the record $460 billion cloud backlog converts into recognized revenue as capacity comes online through 2026 and 2027; whether rising depreciation from the capex surge weighs on margins before AI revenue catches up; and whether Gemini Robotics gains hardware partners beyond Boston Dynamics and Agile Robots, or ever becomes a disclosed business line separate from Other Bets.
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Frequently asked questions
Alphabet Inc. is the publicly traded parent company, ticker GOOGL. Google is its largest subsidiary, encompassing Search, Cloud, YouTube, and Gemini. The names are used interchangeably here because Google represents the vast majority of Alphabet's revenue and AI infrastructure spending.
Google has designed its own Tensor Processing Units since 2015. Owning the chip design lets it avoid paying Nvidia's margins on a large share of its AI compute and optimize hardware specifically for its Gemini models, though Google still uses Nvidia GPUs for some workloads and customers.
CEO Sundar Pichai said Google could not build data centers fast enough to meet demand for its AI services in early 2026, meaning cloud revenue would have been higher if more capacity had been available, a supply problem rather than a demand problem.
No. Through Google DeepMind, Google develops Gemini Robotics, AI foundation models that other companies' hardware runs on, including Boston Dynamics' Atlas and Spot and Agile Robots' industrial platforms. It does not manufacture or sell robot hardware itself, and doesn't disclose robotics revenue separately from the Other Bets segment, which also includes Waymo.
This page describes public value-chain positioning for informational purposes only. It is not investment advice, and inclusion here is not a recommendation to buy or sell any security. Figures reflect public reporting as of mid-2026 and may have changed since.