June 28, 2026, (Inside AI) — Google has imposed limits on Meta’s access to its Gemini AI models, the Financial Times reported Sunday. The move came after Meta sought more computing capacity than Google could supply, disrupting internal AI projects at the social media giant.
Google, owned by Alphabet, informed Meta around March that it could not fulfill the full Gemini capacity Meta had requested to purchase. The shortfall specifically delayed certain AI initiatives inside Meta, according to the report, which cited people familiar with the matter.
Several other Google Cloud clients also faced restrictions, though to a lesser extent. Meta was disproportionately affected because of its exceptionally high demand for Google’s models, the FT noted. Reuters could not independently verify the report, and both Google and Meta did not immediately respond to requests for comment outside business hours.
The constraints highlight a growing tension in the AI industry: even as companies pour billions into chips and data centers, computing power remains a bottleneck. Google Cloud’s revenue hit $20 billion in the first quarter ended March, but CEO Sundar Pichai said capacity limits prevented even higher growth. The cloud unit’s backlog nearly doubled quarter over quarter, he added.
In response to the restrictions, Meta has urged staff to use AI tokens more efficiently. Tokens are the units that measure AI usage, and the directive signals a scramble to optimize resources amid the shortage.
The episode underscores how even the largest tech firms are not immune to infrastructure constraints. While Google develops its own AI accelerators, the surging demand for Gemini—a family of multimodal models—has outpaced its ability to provision capacity for external customers.
Industry analysts note that such rationing could accelerate Meta’s push toward self-sufficiency. The company has been building its own AI chips and expanding data center investments, but those efforts take years to materialize. In the interim, reliance on cloud providers remains a strategic vulnerability.
The FT report did not detail which specific Meta projects were delayed, but the company has been integrating generative AI across its platforms, including chatbots and ad tools. Any slowdown could ripple through product roadmaps and competitive positioning.
For Google, the capacity crunch is a double-edged sword. It validates the intense demand for its AI offerings but also risks pushing key clients to seek alternatives. Rivals like Microsoft Azure and Amazon Web Services are aggressively expanding their AI infrastructure, and any dissatisfaction could shift market share.
The situation also raises questions about the scalability of cloud-based AI services. As models grow larger and more complex, the physical limits of data centers—power, cooling, and chip availability—become critical factors. Industry watchers expect these constraints to persist through 2026 and beyond.
Sofia spent years studying how machines parse human language before pivoting to cover the systems now rewriting it. She reads technical papers the way others read news and looking for what the numbers are actually saying beneath the headlines.
