Google limits Meta’s Gemini access as AI compute shortages bite

Google has limited Meta’s use of Gemini after Meta asked for more computing capacity than Google could provide, a sign that the AI race is now being shaped by scarcity as much as ambition. Google told Meta around March that it could not meet the full capacity Meta wanted to purchase, and the shortfall disrupted and delayed some of Meta’s internal AI projects. Several other Google customers were also affected, though Meta was hit more sharply because of its unusually high demand.

The pressure on Google Cloud shows how tight the market has become. On Alphabet’s April 29 earnings call, the company said Google Cloud revenue rose 63% year over year to more than $20 billion in the first quarter of 2026, while backlog nearly doubled to more than $460 billion. Sundar Pichai said Google Cloud was “compute constrained” in the near term and that revenue would have been higher if Google could meet demand. The company’s own growth is now colliding with the limits of physical infrastructure, from chips and servers to data-center capacity.

Meta has spent heavily to build its own AI future, but the company’s dependence on outside capacity has not gone away. On April 29, Meta said it had released its first model from Meta Superintelligence Labs and was on track to deliver “personal superintelligence” to billions of people. Even so, an August 2025 account showed Meta AI leaders had explored using Google’s Gemini model inside Meta AI and had discussed OpenAI models for some app features, underscoring how aggressively Meta has been willing to look beyond its own stack when it needs more capability.

The restrictions point to a broader power shift inside the AI boom. The companies with the deepest pockets can still be throttled by a rival’s supply of compute, and that makes access to cloud capacity and frontier models a strategic vulnerability, not just a purchasing problem. For Meta, a bottleneck on Gemini can slow experimentation, delay products and force harder choices about where to spend scarce tokens and processing time. For Google, the same shortage is both an opportunity and a constraint: demand is strong, but the business cannot fully monetize it unless it can keep enough infrastructure online.