Google restricts Meta’s access to Gemini AI amid capacity constraints
Summarized by AI; it may make mistakes. Check important info
Summarized by AI; it may make mistakes. Check important info

Google has restricted Meta’s access to its Gemini artificial intelligence models after the social media giant requested more AI computing capacity than Google could provide. The move reportedly delayed some of Meta’s internal AI projects and highlighted the growing pressure on AI infrastructure as demand for computing power continues to rise.
Capacity shortage impacts Meta’s AI projects
According to a Financial Times report, the restrictions came into effect around March after Google was unable to meet Meta’s increasing demand for AI computing resources. Meta, one of Google’s largest AI customers, was reportedly affected more than other clients because of its heavy reliance on Gemini models.
Meta asks employees to reduce AI usage
To cope with the limited computing resources, Meta has encouraged employees to use AI tools more efficiently by reducing the consumption of AI tokens, which are used to measure the usage of generative AI models. The report also stated that other Google customers have faced similar capacity constraints, although the impact on them has been less severe.
Rising AI demand puts pressure on infrastructure
The development reflects the growing strain on AI infrastructure as technology companies continue to invest heavily in generative AI. Despite spending billions of dollars on data centres and advanced chips, demand for AI computing power has continued to outpace available capacity.
Google also acknowledges infrastructure challenges
Google has also admitted that computing capacity remains a challenge. During Alphabet’s first-quarter earnings, Google Cloud reported revenue of $20 billion. However, CEO Sundar Pichai said infrastructure constraints prevented stronger growth and contributed to a significant increase in the cloud division’s backlog.
The reported restrictions on Meta’s access to Gemini AI models underscore how shortages in AI infrastructure are emerging as a major challenge for the technology industry as companies continue to expand their AI capabilities.