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Google has reportedly placed limits on Meta's use of its Gemini AI models after the social media company requested more AI computing capacity than the cloud provider could supply, underscoring the growing infrastructure constraints facing even the largest technology companies.
According to a report by the Financial Times, Google informed Meta around March that it would be unable to meet the full Gemini model capacity the company wanted to purchase. The shortfall reportedly disrupted and delayed some of Meta's internal AI projects.
The report said other Google customers have also been affected by capacity constraints, although Meta has faced a larger impact because of its unusually high demand for Google's AI models.
The restrictions have prompted Meta to encourage employees to use AI tokens more efficiently, according to the report. AI tokens are the units of text processed by large language models and are increasingly used to determine consumption and pricing for AI services.
The reported capacity crunch highlights the widening gap between surging enterprise demand for AI services and the availability of the computing infrastructure needed to support them. Technology companies continue to invest tens of billions of dollars in AI chips and data centers, yet demand for graphics processing units (GPUs) and AI compute continues to outstrip supply.
Google has previously acknowledged that infrastructure constraints are limiting the growth of its cloud business. In its first-quarter earnings, Google Cloud reported revenue of $20 billion, while CEO Sundar Pichai said compute capacity shortages prevented even stronger growth and contributed to a sharp increase in the division's backlog.
The reported restrictions on Meta's Gemini usage illustrate how AI infrastructure availability is becoming a competitive bottleneck, even among the industry's largest technology companies, as demand for generative AI models continues to accelerate.
According to a report by the Financial Times, Google informed Meta around March that it would be unable to meet the full Gemini model capacity the company wanted to purchase. The shortfall reportedly disrupted and delayed some of Meta's internal AI projects.
The report said other Google customers have also been affected by capacity constraints, although Meta has faced a larger impact because of its unusually high demand for Google's AI models.
The restrictions have prompted Meta to encourage employees to use AI tokens more efficiently, according to the report. AI tokens are the units of text processed by large language models and are increasingly used to determine consumption and pricing for AI services.
The reported capacity crunch highlights the widening gap between surging enterprise demand for AI services and the availability of the computing infrastructure needed to support them. Technology companies continue to invest tens of billions of dollars in AI chips and data centers, yet demand for graphics processing units (GPUs) and AI compute continues to outstrip supply.
Google has previously acknowledged that infrastructure constraints are limiting the growth of its cloud business. In its first-quarter earnings, Google Cloud reported revenue of $20 billion, while CEO Sundar Pichai said compute capacity shortages prevented even stronger growth and contributed to a sharp increase in the division's backlog.
The reported restrictions on Meta's Gemini usage illustrate how AI infrastructure availability is becoming a competitive bottleneck, even among the industry's largest technology companies, as demand for generative AI models continues to accelerate.
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