Google informed Meta around March that it could not provide all the Gemini AI capacity Meta wanted to purchase, according to sources cited by the Financial Times. The shortfall has disrupted and delayed some of Meta's internal AI projects.
The development underscores how surging demand for advanced AI models is turning computing power into the tech industry's most constrained resource. Even a company as large as Meta, which invests heavily in AI, faces bottlenecks when competing for limited cloud infrastructure.
Google's decision to cap capacity stems from its own growing internal demand for Gemini and commitments to other major clients. The precise scale of the shortfall was not disclosed, but the impact has been significant enough to force Meta to adjust timelines for certain AI initiatives.
For Meta, the setback could slow its efforts to integrate advanced AI across products like social platforms and metaverse tools. The company may need to seek alternative cloud providers or accelerate development of its own AI chips to reduce dependency on Google.
Meta declined to comment on the report, while Google did not respond to a request for comment. The incident highlights how even large tech firms struggle to secure sufficient AI computing power in a hypercompetitive market.