Google has limited Meta’s access to its Gemini AI models due to compute constraints, severely impacting the social media company, the Financial Times reported. As a result, Meta has announced a shift toward its internal Muse Spark model to reduce dependence on external providers.
The restrictions forced Meta to instruct staff to use AI tokens more efficiently, according to three sources cited by the Financial Times. Both Google and Meta declined to comment on the situation.
Meta had been relying on Gemini to automate safety processes, including content moderation and scam removal. The shift towards Muse Spark aims to alleviate reliance on Google’s AI resources amidst ongoing compute shortages.
Google’s own computing limitations have led the company to pay SpaceX $920 million a month for access to 110,000 Nvidia GPUs, referred to as “bridge capacity” for its Gemini Enterprise. This underscores how the current AI compute shortage is affecting the relationships among major companies in the sector.
Despite Google’s investments in AI infrastructure totaling over $180 billion this year, it has not been able to meet all customer demand. The company is rationing access to customers like Meta while simultaneously securing GPU capacity from SpaceX.
Meta’s situation reflects its ongoing transition from dependency on its competitor’s AI models to developing internal alternatives. In May, Meta laid off 8,000 employees and redirected significant resources toward its own AI infrastructure, projecting capital expenditures between $115 billion to $135 billion for 2026. The company has reassigned 7,000 workers to AI-focused roles and recently launched the Muse Spark model under its Superintelligence Labs.
This transition aligns with a broader industry trend where demand for AI compute continues to outstrip the capacity provided by major players. Companies like Anthropic are also looking for solutions, such as renting data centers from SpaceX, to meet their AI operational needs.
The overall pattern indicates that the physical infrastructure required to support AI algorithms and talent remains the bottleneck in the AI boom, surpassing all prior expectations for infrastructure spending.
