Economy June 28, 2026 01:23 AM

Google Restricts Meta's Access to Gemini Models, Causing Internal Delays

Capacity shortfall at Google Cloud forces Meta to ration AI usage and adjust internal projects

By Caleb Monroe
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Google has limited Meta's access to its Gemini AI models after Meta sought more computing capacity than Google could supply, prompting Meta to push staff to use AI tokens more efficiently and delaying some internal AI initiatives. The capacity gap also affected other Google customers to a lesser degree, while Google Cloud reported $20 billion in first-quarter revenue and a near doubling of backlog tied in part to computing constraints.

Google Restricts Meta's Access to Gemini Models, Causing Internal Delays
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Key Points

  • Google told Meta around March it could not supply the full Gemini model capacity Meta sought, prompting usage restrictions.
  • The capacity shortfall disrupted and delayed some of Meta’s internal AI projects; several other Google clients were also affected to a lesser degree.
  • Google Cloud generated $20 billion in revenue for the quarter ended March, with CEO Sundar Pichai attributing constrained computing power to capped growth and a near doubling of backlog quarter on quarter.

Google has imposed limits on Meta's consumption of its Gemini AI models after the social media company requested more model capacity than Google could provide, according to a Financial Times report. The restriction, which Google communicated to Meta around March, left some of Meta's internal AI projects disrupted or delayed.

The report says several other Google clients experienced similar restrictions, though the impact on those customers was smaller. Meta's situation was singled out because of its exceptionally high demand for access to Google’s Gemini models.

As a result of the limits, Meta has told employees to be more conservative with "AI tokens" - the units used to measure and bill AI usage - in an effort to stretch available capacity while internal projects proceed.

The Financial Times account also notes wider industry strain: even as firms invest billions in chips and data centres, demand for computing power to run AI workloads is outpacing supply. Google Cloud reported $20 billion in revenue in the first quarter ended March, but Chief Executive Officer Sundar Pichai said that constraints on computing capacity held back even stronger growth and helped push the cloud unit's backlog to nearly double quarter on quarter.

The report could not be immediately verified, and both companies did not immediately respond to requests for comment outside business hours, the report said.


Context and implications

The limits placed on Meta's usage of Gemini models reflect a wider bottleneck in AI infrastructure capacity. The details in the report emphasize that allocation decisions by major cloud providers can have disruptive effects on large-scale internal AI development programs. The situation also illustrates how cloud capacity constraints can shape growth outcomes for cloud vendors themselves, as cited by Google Cloud's backlog and CEO commentary.

What was reported

  • Google informed Meta around March that it could not fulfil the full Gemini capacity Meta wanted to buy.
  • The shortfall disrupted and delayed some of Meta's internal AI work.
  • Other Google customers were affected to a lesser extent.
  • Meta has encouraged staff to conserve AI tokens to manage the restriction.
  • Google Cloud posted $20 billion in revenue for the quarter ended March, with capacity limits cited by the CEO as a factor in both growth moderation and a near doubling of the cloud backlog quarter on quarter.

Data notes

The article presents reported developments about access limits, internal effects at Meta and related comments about Google Cloud's revenue and backlog. The limitations on verification and the companies' lack of immediate comment are noted in the report.

Risks

  • Ongoing computing capacity constraints could continue to delay AI development efforts at large tech firms, affecting product timelines and internal projects - impacting technology and cloud services sectors.
  • Allocation limits by major cloud providers may force customers to alter usage patterns or invest in alternate infrastructure, introducing uncertainty for enterprise AI deployments - impacting enterprise IT spending and data centre investment decisions.
  • If verification of the reported restrictions remains limited, uncertainty about the extent and duration of access limits could affect customers' planning and vendor relationships - impacting market perceptions in the cloud and AI services space.

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