Google has reportedly placed limits on Meta’s use of its Gemini AI models after the social media company requested additional computing capacity that Google was unable to fully provide, according to a report by the Financial Times.
The report stated that Google informed Meta around March that it could not meet the full Gemini AI capacity the company had sought to purchase. The shortage reportedly disrupted and delayed some of Meta’s internal AI initiatives and development programs.
The development highlights the growing pressure on global AI infrastructure as major technology companies compete for access to high-performance computing resources required to train and run large-scale artificial intelligence models.
According to the report, the restrictions also prompted Meta to encourage employees to use AI tokens more efficiently. AI tokens are units used to measure and manage AI model usage and computational consumption.
The reported capacity constraints come as demand for generative AI services, AI agents, enterprise AI tools, and large language models continues to surge across industries, significantly increasing pressure on cloud infrastructure and GPU availability.
Google has been rapidly expanding its AI and cloud business through Gemini models and Google Cloud services. However, the company has previously acknowledged infrastructure limitations affecting growth opportunities.
Revenue at Google Cloud reportedly reached $20 billion during the quarter ended March, though Google CEO Sundar Pichai said computing power constraints prevented even stronger growth performance.
The situation also reflects the increasingly complex competitive relationship among major technology companies, where rivals often rely on each other’s cloud infrastructure, AI models, and semiconductor ecosystems despite competing directly in consumer and enterprise AI markets.
Meta has been aggressively expanding its AI capabilities across generative AI, large language models, AI assistants, advertising technologies, and enterprise AI infrastructure as competition intensifies among global AI leaders including Google, Microsoft, OpenAI, Anthropic, and Amazon.
The reported limitations underscore broader industry challenges surrounding GPU shortages, AI infrastructure scaling, energy requirements, and the massive computing resources needed to support next-generation AI development and deployment.
