Meta Faces Gemini Access Limits as Google Struggles With Rising AI Capacity Demand

Key Highlights

• Google has reportedly limited Meta’s access to Gemini AI models due to AI computing capacity constraints, according to a Financial Times report.

• The reported restriction affected some of Meta’s internal AI projects, while Google and Meta have not confirmed the claims.

• The situation reflects rising demand for AI infrastructure, including advanced chips, cloud resources, and data centers.

Google has reportedly limited Meta’s access to its Gemini AI models after Meta requested more computing capacity than Google could provide, according to a Financial Times report published on June 28. The report said Google informed Meta earlier this year that it could not provide the full amount of AI computing resources requested for Gemini access.

According to the report, the shortage affected some of Meta’s internal AI projects and led the company to manage its AI computing usage more carefully. The report cited people familiar with the matter and said Meta even asked employees to use AI computing resources more carefully because of limited capacity.

Reuters said it could not independently verify the Financial Times report. Google and Meta have not publicly confirmed the claims, yet. The information is based on sources cited by the Financial Times rather than official statements from either of the companies.

The report comes as the technology industry faces rising pressure on AI infrastructure. Building and operating advanced AI models automatically requires large amounts of computing power, including high-performance chips, data center capacity, networking systems, and energy resources.

Major technology companies such as Google, Meta, Microsoft, Amazon, and OpenAI are increasing their AI infrastructure spending as demand for AI services grows. However, expanding this infrastructure takes time because companies need to build data centers, secure chip supply, and increase computing availability.

Google has invested heavily in its AI infrastructure through Google Cloud, custom Tensor Processing Units (TPUs), Nvidia GPUs, and new data center projects. These resources support Google’s Gemini models, cloud services, and other AI products.

During Alphabet’s first-quarter earnings call, CEO Sundar Pichai said demand for Google Cloud services remained higher than available computing resources. He said infrastructure limits affected Google’s ability to meet customer demand and added to a backlog of orders.

Google Cloud revenue reached $20 billion in the first quarter, showing strong demand for the company’s cloud and AI services. However, Sundar Pichai said limited computing capacity prevented even stronger growth and contributed to the cloud unit’s backlog nearly doubling compared with the previous quarter.

Google has continued increasing its AI infrastructure spending as it competes in the AI market. The company is focused on expanding computing capacity for both internal AI development and external customers using Google Cloud.

Meta has also increased its AI infrastructure investment. The company has been purchasing GPUs, expanding data center capacity, and developing its own AI models. CEO Mark Zuckerberg has described AI infrastructure as a major investment area for Meta.

Meta’s AI strategy includes developing its own Llama models while also using external AI resources when needed. The reported Gemini capacity issue shows how even large technology companies may depend on outside infrastructure when demand for computing power increases.

The situation also reflects a larger shift in the AI industry. Companies are competing not only on AI models and software but also on access to the physical resources needed to support them.

As AI adoption expands, the availability of chips, cloud capacity, and data centers will continue affecting how quickly companies can train, test, and launch new AI systems.

Sources: Financial Times; Reuters; CNBC; CIO Tech Outlook; Alphabet Investor Relations; Google Cloud; Meta Investor Relations.

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