Google capped Meta’s Gemini access due to compute constraints. Meta told staff to use AI tokens more efficiently and is shifting to its own Muse Spark model.

Google has placed limits on Meta’s use of its Gemini AI models because it cannot provide as much computing capacity as the social media company wanted, the Financial Times reported on Sunday. The restrictions have affected several Google clients, with Meta hit particularly hard.

The move has had a knock-on effect on Meta’s internal projects. The company has told staff to make more efficient use of AI tokens, according to three people familiar with the matter cited by the FT. Both Google and Meta declined to comment.

Meta had initially relied on Gemini, which proved better than its own Llama open-source models, to automate safety processes like removing harmful content and wiping out scams. It has increasingly been shifting workloads to Muse Spark, a new internal model, as it looks to reduce dependence on external AI providers. Google itself is so compute-constrained that it agreed to pay SpaceX $920 million a month for access to 110,000 Nvidia GPUs, calling it “bridge capacity” to meet surging demand for Gemini Enterprise.

The situation illustrates how the AI compute shortage is reshaping relationships between the industry’s largest companies. Google, which owns one of the world’s largest pools of AI infrastructure and is spending over $180 billion on capex this year, still cannot serve all of its customers’ demand. That it is rationing access to a company as large as Meta, while simultaneously renting GPUs from a rocket company, is the clearest signal yet that AI infrastructure buildouts have not kept pace with consumption.

For Meta, the dependence on a competitor’s AI models was always an uncomfortable arrangement. The company cut 8,000 jobs in May and redirected billions toward AI infrastructure, with capex guidance of $115 to $135 billion for 2026. It has reassigned 7,000 workers to AI-focused roles and launched Muse Spark under its Superintelligence Labs division. The Gemini restrictions accelerate a transition Meta was already pursuing, from relying on external frontier models to building internal alternatives capable of handling critical workloads like content moderation at scale.

The broader pattern is consistent across the industry. Demand for AI compute is growing faster than even the most aggressive infrastructure spending can supply. Google is buying capacity from SpaceX. Anthropic is renting an entire data centre from SpaceX. Meta is being told to use fewer tokens by its own cloud provider. The AI boom’s most tangible bottleneck is not algorithms or talent. It is the physical infrastructure required to run them.