Seizing the "Gemini 3" momentum, "Google Chain" challenges "Nvidia Chain," disrupting the AI trading landscape.

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Google is leveraging its latest breakthroughs in the AI model field to launch a comprehensive challenge to Nvidia's dominant position in chips. The search giant has begun pitching solutions to major clients like Meta to deploy TPU chips in their own data centers, attempting to expand this alternative AI chip from Google's cloud rental business to a broader market.

According to the latest media reports citing insiders, Meta is in talks with Google to use billions of dollars worth of TPU chips in its data centers in 2027, and is also planning to rent chips from Google Cloud next year. This potential deal could allow Google to capture 10% of Nvidia's annual revenue, bringing billions in additional income, a goal some in the leadership of Google Cloud have expressed internally.

Google’s recent release of the Gemini 3 large language model has triggered a strong market reaction. This model is mainly trained on TPU chips, with performance approaching or even surpassing OpenAI's ChatGPT. This technological breakthrough has prompted investors to reassess the landscape of the AI chip market. Google shares surged 6.3% in a single day to a record high of $318.58, with a cumulative gain of 68% this year, while Nvidia’s share price fell nearly 10% this month, narrowing the market value gap between the two to $526 billion, the smallest since April last year.

Nvidia CEO Jensen Huang has acted swiftly to counter this threat. According to insiders, after Google reached an agreement with Anthropic to provide up to a million TPUs, Huang immediately announced an investment of several billion dollars in the company and secured its continued use of Nvidia GPUs. Similarly, after news emerged that OpenAI plans to rent TPUs from Google Cloud, Huang also reached an investment agreement of up to $100 billion with OpenAI.

Google TPU Strategy Upgrade: From Cloud Leasing to On-Premises Deployment

Google has long been renting out its self-developed Tensor Processing Unit (TPU) chips to cloud customers, who used the chips in Google Cloud data centers. But now, Google has begun pitching a new solution to clients including Meta and major financial institutions: letting them use TPUs in their own data centers. According to insiders, Meta is currently in talks with Google to rent Google Cloud chips next year and to deploy TPUs in Meta data centers by 2027. Meta currently mainly relies on Nvidia's graphics processing units.

Sources familiar with the matter revealed that Google emphasizes to enterprises that clients wish to deploy chips in their own data centers to meet higher security and compliance standards, especially when handling sensitive data. Google also points out that TPUs may be particularly helpful for high-frequency trading firms running AI models in their own facilities.

To support this new initiative called "TPU@Premises", Google has developed software called "TPU command center" to make it easier for customers to use these chips. This software is seen as Google’s major weapon against Nvidia's biggest advantage—Cuda software. Cuda has become the de facto standard for AI developers, who are very familiar with using it to run models on Nvidia chips. Although Google's Jax programming language is less familiar to developers, Google has informed customers that they can use software related to PyTorch to operate TPUs without having to become Jax experts.

Google became more proactive in expanding the TPU market last summer. It began reaching out to smaller cloud service providers who primarily rent Nvidia chips, proposing that they host TPUs in their data centers. Google has at least reached an agreement with one such provider—London’s Fluidstack—to host TPUs in a New York data center. As part of the agreement, Google also provided an irresistible clause: If Fluidstack is unable to pay the upcoming rent for the New York data center, Google will backstop up to $3.2 billion.

Gemini 3 Breakthrough Reshapes Market Confidence

Google’s current surge in the AI field could assist its TPU rollout. The latest large language model Gemini 3 released earlier this month has received rave reviews from some top tech figures, who believe Google has closed its technology gap with OpenAI. Reportedly, Gemini 3 is faster, sharper, and has deeper reasoning capabilities than OpenAI’s ChatGPT, Elon Musk’s Grok, and Jeff Bezos-backed Perplexity. Its pricing is on par with or lower than competing AI models.

More importantly, Gemini 3 is mainly trained on Google's TPUs rather than competitors’ reliance on Nvidia chips. Although TPUs are less flexible than Nvidia GPUs, their development costs are lower and their power consumption is smaller at full load. Melius Research tech strategist Ben Reitzes said: "Some investors are very worried that, due to the huge improvements in the Gemini model and the ongoing advantages of custom TPUs, Alphabet will win the AI war."

Some developers believe that with TPUs, Google has narrowed Nvidia's lead in powering the intensive server clusters needed to train new large AI models. Sources reveal that Meta is discussing using TPUs with Google to train new AI models, not just for inference support of existing Meta models. This is notable as most analysts have previously argued that the biggest challenge to Nvidia lay in inference chips, while its hold on the training chip market was considered unshakeable.

D.A. Davidson analyst Gil Luria estimates that if Google's DeepMind AI research lab and TPU sales business were separated into independent divisions, their value would approach $1 trillion, making them "arguably one of Alphabet’s most valuable businesses". The surge in Google’s stock price reflects this expectation, with a 68% increase this year far outpacing the "Magnificent Seven" index’s 22% and the Nasdaq Composite’s 18%. Broadcom, Google’s TPU manufacturing partner, has also risen more than 63% this year.

Nvidia Mounts Defensive Counterattack

Regardless of whether Google's TPU effort succeeds, the specter of a powerful Nvidia alternative may already benefit large clients like Anthropic and OpenAI who do not wish to depend on a single AI chip supplier. Last month, after Google agreed to provide Anthropic up to a million TPUs, Jensen Huang announced another multibillion-dollar investment in Anthropic, securing its continued use of Nvidia GPUs.

Similarly, after news that OpenAI plans to rent TPUs from Google Cloud became public, Huang reached a preliminary agreement to invest up to $100 billion in OpenAI to help develop its own data centers, and discussed renting Nvidia GPUs to the company. A Nvidia spokesperson said the company’s investments in AI startups do not require these companies to buy its GPUs. Huang may preemptively strike an agreement with Meta to prevent it from making a TPU deal with Google, as Meta is already one of Nvidia’s biggest customers.

Huang has acknowledged Google’s progress in AI chips. In a podcast last fall, he told investor Brad Gerstner that, considering Google has made seven generations of TPUs, "We have to give respect where it’s truly due." Nvidia has also made similar financial commitments to AI cloud partners such as CoreWeave.

Meta’s spokesperson declined to comment on the TPU negotiations. Google spokesperson did not comment on TPU efforts, but said the company "is experiencing accelerating demand for our custom TPUs and Nvidia GPUs; we are committed to supporting both, as we have been for years".

Market Structure Faces Reshuffling

According to Wind Trading Desk, Rich Privorotsky, a trader at Goldman Sachs Global Banking & Markets, pointed out in his latest report that Google’s Gemini 3 model is a "disruptive model" reshaping the entire AI investment ecosystem, causing other companies’ product cycles to be delayed, capital expenditures to rise, and return on investment to become more uncertain. The trader emphasized that although Nvidia’s financial data remains strong, it is no longer the core focus of AI investment.

Melius Research’s Reitzes warned: "It’s too soon to declare Alphabet the long-term AI winner based on recent progress. That said, semiconductor and hyperscale cloud computing companies (especially Oracle) need to be aware that the ‘Alphabet problem’ is a worrying factor." Oracle has purchased billions of dollars of Nvidia chips to rent out via the cloud. If others build AI cloud competitors, cheaper TPUs mean it could be undercut on price.

Even a modest narrowing of Nvidia’s AI advantage could send shockwaves through the markets in the coming months. If it turns out that lower-cost chips can perform just as well, companies that have invested heavily in Nvidia semiconductors may get buyer’s remorse. Valuations are already very high, from publicly traded hyperscale cloud companies to OpenAI, while how new technology will benefit the real economy remains in question. In fact, according to an internal memo published by The Information last week, OpenAI CEO Sam Altman acknowledged that Google’s AI advances could bring "some temporary economic headwinds" to the company: "I expect things outside will be tough for a while."

Bernstein senior analyst Stacy Rasgon said in an interview with CNBC: “We’re not at the point where we need to care who wins or loses yet. More important is ‘are the opportunities in front of AI sustainable?’ If they are, they’ll all be fine; if not, they’ll all have trouble.”

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