Google’s TPU Business Outpaces Rivals as Hyperscalers Accelerate Custom AI Silicon Strategies
Executive Summary:
Google parent Alphabet’s emerging business of selling artificial intelligence (AI) accelerator chips is twice as large as a cloud computing rival, Google executive Thomas Kurian claimed Tuesday at a Goldman Sachs investors conference.
On July 22, Google reported second-quarter cloud-computing revenue of $24.77 billion, up 82% year over year, driven by artificial intelligence workloads, handily beating estimates of $22.46 billion. For the first time, Google included third-party sales of AI accelerator chips, called tensor-processing units, in cloud revenue.
Kurian, head of Google’s cloud business, made these remarks at Goldman Sachs’ Communacopia conference:
“We offer the best computational infrastructure for AI, and we offer 3 types of silicon. NVIDIA GPUs, our own Tensor Processing Units (TPUs), custom ARM silicon. [As AI models generate code awe also offer our own Arm processors to run that code.] We offer 2.7x better price performance for training, 80% better price performance for inference, 30% better price performance for CPUs. All of that allows us to differentiate our portfolio from other providers. It allows us to offer solutions to financial markets and capital markets.”
“The size of our accelerator business, our TPU business, is more than twice that of the next-largest hyperscaler.”
“Our platform is called Gemini Enterprise. It is used by over 90% of the Fortune 100 and thousands of small businesses. It’s used in a very specific way. People want to use it as a reasoning agent. So break the plan, understand the steps that are needed, reason on it and execute the steps. So it uses a reasoning agent to understand all the information in the company to then automate that workflow process. And when it does it, you want strong controls. What kinds of controls? Companies are worried about security. They’re worried about auditing, what these agents are doing. They want to manage costs and set budget caps. We have all those controls. And we allow people to use the right model for the right task. So you don’t have to always use the most expensive model, saving people a lot of money in doing so. We have a range of companies from insurance.”
–>You can read the entire transcript here. For more on Google’s TPUs please see:
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Google’s TPU photo
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Kurian added that Google monetizes TPU systems through three business models. One is letting companies rent TPU processing at its own cloud business. Also, Google sells TPU systems directly for deployment in customers’ data centers. One such customer is Anthropic. Third, Google also sells TPUs through a Blackstone cloud computing joint venture.
Google’s AI Chip Business:
In May, Google introduced Ironwood, its eighth-generation of TPUs. The Ironwood TPUs target both training of AI models and “inferencing” — processing AI workloads.
In a report published Aug. 24, Morgan Stanley analyst Brian Nowak estimated that Google cloud could garner $84 billion in “first party” — meaning non-cloud rental — TPU sales in 2027.
“We are raising our TPU sale estimates to $27 billion selling at a 30% gross margin,” Nowak said. “In all, we now expect Google to sell 0.3 gigawatts of TPU systems in the second half of 2026, 3.2 gigawatts in 2027 and 4.2 gigawatts in 2028. This translates into $84 billion/$108 billion of TPU-related Google cloud revenue in 2027 and 2028.”
In Q2-2026, Google said its cloud computing order backlog jumped to $514 billion, up from $460 billion in Q1. The backlog is converted into realized revenue as new data centers come online and crunch artificial intelligence-related workloads — training AI models and processing AI apps.
Google has increased its 2026 capital spending guidance to a range of $195 billion to $205 billion. Most of the spendings is going toward AI data centers and AI model development. In Q2, capital spending jumped 100% from a year earlier to $44.9 billion.
Kurian, a former top executive at Oracle, took over as the cloud-computing unit’s CEO in November 2018. When Kurian arrived, Google’s cloud customers were mostly other tech companies. Under Kurian, Google has targeted enterprise customers with cloud-based data-analytics and artificial intelligence tools.
Hyperscaler Custom Silicon: Meta, Microsoft, Oracle, and the Shift to In-House AI Accelerators:
While Google’s TPU business has reached a scale that Kurian says is more than twice that of the next-largest hyperscaler, other cloud and platform operators are rapidly expanding their own custom AI silicon programs to reduce dependence on Nvidia GPUs and optimize cost, power, and workload-specific performance.
Amazon.com has developed in-house Trainium AI accelerators while Microsoft has developed Maia AI chips. Amazon is further ahead than Microsoft in selling AI chips to outside customers, analysts say.
Meta – MTIA Family Targets Inference at Scale:
Meta has moved aggressively into custom silicon with its Meta Training and Inference Accelerator (MTIA) family, announcing four new chips — MTIA 300, 400, 450, and 500 — in March 2026 as part of a strategy to diversify hardware sources and lower AI infrastructure costs. The MTIA 300 entered production in mid-2026, with subsequent generations rolling out on an approximately six-month cadence through 2027.
Meta’s MTIA chips are manufactured by TSMC and co-developed with Broadcom under a multi-year partnership extending through 2029. The roadmap spans ranking and recommendation training (MTIA 300), combined generative AI and ranking workloads (MTIA 400), and decode-optimized generative AI inference (MTIA 450 and 500), with mass deployment of the flagship MTIA 500 planned for late 2027. Meta plans to put its own AI chip into production in September and is aiming to roughly double the computing capacity across its data centres.
By mid-2026, Meta, Amazon, Microsoft, and OpenAI have each closed the gap on the three key AI inputs — custom chips, power, and models — that only Google held in 2021.
Microsoft: Maia 200 and the Push to External Customers:
Microsoft unveiled its first custom AI accelerator, Maia 100, at Hot Chips 2024, followed by the inference-optimized Maia 200 in January 2026. Maia 200, built on TSMC’s 3 nm process with more than 140 billion transistors, 216 GB of HBM3e, and over 10 PFLOPS of FP4 compute within a 750 W SoC TDP, is designed to deliver 30% better performance per dollar for AI token generation.blogs.
Maia 200 will serve multiple models, including OpenAI’s GPT-5.2, and support Microsoft Foundry, Microsoft 365 Copilot, and reinforcement learning workflows. Microsoft plans to unveil next-gen Maia 300 AI chip in September, aiming to lower costs for in-house and OpenAI models while actively courting major enterprise customers. Anthropic is reportedly in talks with Microsoft to rent the company’s custom AI server chips as it looks to expand computing capacity.blogs.
Oracle: Partner-Led AI Clusters Rather Than Custom Silicon:
Oracle has taken a different path, opting not to develop its own AI accelerator but instead building large-scale AI clusters using third-party chips from Nvidia and AMD. Oracle plans to install the first MI450-equipped Helios racks in its OCI data centers during the third quarter of 2026, with an initial deployment targeting 50,000 MI450 processors.
Oracle’s AI strategy emphasizes rapid deployment of massive GPU-based clusters to serve anchor tenants like OpenAI under a reported $300 billion, five-year cloud computing contract beginning in 2027. In parallel, OpenAI is diversifying its supply of compute by designing its own chips with partners like Broadcom, with the first custom AI inference chips expected to deploy in the second half of 2026.
Market Implications:
By 2026, five of six major AI players — Google, Meta, Amazon, Microsoft, and OpenAI — now control at least two of the three critical AI inputs (chips, power, models), down from only Google in 2021. This vertical integration trend is reshaping the AI infrastructure market, with hyperscalers increasingly using custom silicon to optimize cost and performance for specific workloads while maintaining strategic flexibility through multi-vendor GPU procurement.
Google’s TPU v7, Amazon’s Trainium 3, Microsoft’s Maia 2, and Meta’s MTIA 2 all ramped into volume production in 2025–2026, signaling a maturation of the hyperscaler custom silicon ecosystem. Meta is also the first commercial gigawatt AMD MI450 deployment in H2 2026, illustrating a hybrid approach that combines in-house accelerators with third-party GPUs.
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References:
https://www.investors.com/news/technology/google-stock-cloud-kurian-ai-chip-business/
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