TL;DRGoogle 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.The 💜 of EU techThe latest rumblings from the EU tech scene, a story from our wise ol' founder Boris, and some questionable AI art. It's free, every week, in your inbox. Sign up now!
