The rapid rise of artificial intelligence is creating an unexpected challenge for even the world’s biggest technology companies—there simply isn’t enough computing power to meet the growing demand. According to a report by the Financial Times, Google has reportedly restricted Meta’s access to its Gemini AI models after the Facebook parent sought significantly more AI computing capacity than Google could provide.
Meta had reportedly approached Google to secure additional access to Gemini AI to support its expanding AI initiatives. However, in March, Google informed the company that it could not allocate the full computing resources requested. As a result, some of Meta’s internal AI projects have reportedly experienced delays.
The shortage hasn’t affected Meta alone. Google is also said to be struggling to meet the growing demands of its own cloud customers. To manage the limited resources, Meta has reportedly instructed employees to use AI tokens more efficiently, reducing unnecessary AI workloads and prioritizing critical projects.
The situation highlights a much larger industry-wide problem. As businesses increasingly adopt AI-powered chatbots, coding assistants, and autonomous AI agents, demand for GPUs and large language model (LLM) computing continues to outpace global supply. Despite investing billions of dollars in AI chips, data centres, and cloud infrastructure, major technology companies are still facing capacity constraints.
Google Cloud’s financial performance reflects this growing demand. The company generated nearly $20 billion in revenue during the first quarter, demonstrating strong growth. However, Google CEO Sundar Pichai admitted that the business could have performed even better if additional computing resources had been available.
“Obviously, we are compute-constrained in the near term,” Pichai said, adding that Google Cloud revenue would have been higher if the company had been able to satisfy customer demand.
The shortage could also benefit rival cloud providers such as Amazon Web Services (AWS), Microsoft Azure, Oracle Cloud, and other specialized infrastructure providers, as enterprises look for alternative sources of AI computing power.
At the same time, the soaring cost of AI infrastructure is becoming a growing concern. Running advanced AI models requires enormous computational resources, driving up operating expenses. Reports suggest that several technology giants, including Microsoft, have already introduced measures to limit AI usage internally in an effort to control rapidly increasing AI compute costs.
As AI adoption accelerates worldwide, access to computing power is emerging as one of the industry’s biggest competitive advantages, making GPUs and cloud infrastructure just as valuable as the AI models themselves.
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