Google Limits Meta's Gemini Use Amid AI Demand

Google's infrastructure constraints are limiting Meta's access to Gemini AI models, exposing capacity shortages that could disrupt the cloud computing market's expansion trajectory as demand for inference workloads continues to outpace available resources.

The search engine company imposed strict usage limits after Meta requested more computing capacity than Google could deliver in March 2026, according to three people familiar with the matter who talked to Financial Times.

The restrictions reveal how infrastructure bottlenecks are affecting even the largest technology companies as they compete to deploy AI systems across their operations.

While several other Google clients have experienced similar limitations, Meta has been particularly affected because of its exceptionally high demand for the company's models, which the social media platform uses for safety processes, customer services and internal workflows.

Cloud revenue growth constraints

Demand for AI computing is rising sharply as businesses deploy chatbots, coding assistants and AI agents across their operations, creating inference workloads that have emerged as one of the biggest challenges in the technology sector.

According to Financial Times, the increase in these tasks, which are required to run models after they have been trained, has created severe capacity constraints that are now limiting revenue growth for cloud service providers.

During the first-quarter earnings in April, Sundar Pichai, CEO at Google, CEO of Google, says cloud revenue exceeded US$20bn for the first time. The backlog of signed but undelivered cloud contracts nearly doubled quarter on quarter, to more than US$460bn

Sundar explains that computing power constraints prevented even higher growth of Google Cloud, stating that "obviously, we are compute-constrained in the near term, and as an example, our Cloud revenue would have been higher if we were able to meet the demand."

As a direct result of these demands, particularly from large corporate customers such as Meta, Google is racing to secure additional capacity through external partnerships.

Earlier this month, Google signed a US$920m-a-month deal to lease computing capacity from Elon Musk's SpaceX, while Anthropic, the maker of the Claude chatbot, also signed a similar deal with SpaceX last month.

Strategic shifts reduce supplier dependence

The restrictions expose the extent to which Meta has relied on rival models such as Gemini as the social platform spends aggressively to become a leader in AI and improve its own models.

CEO Mark Zuckerberg has recently been tapping into talent and securing infrastructure to develop what he dubs personal superintelligence, an advanced AI system that surpasses human cognitive capabilities across multiple domains.

Unlike Google, Meta does not possess a cloud business and is racing to build out its fleet of data centres for its own training and inference needs, committing to invest US$600bn in the US by 2028 as part of this push.

Gemini has been used internally at Meta to automate some of its safety processes, such as rooting out scams and taking down harmful content, alongside customer services and advertising help chatbots, as well as for internal workflows and coding with other models like Anthropic's Claude.

Meta initially chose Gemini because it performed better than its own Llama open-source models, but more recently the company has begun prioritising its new Muse Spark model, which is viewed as more competitive with Gemini and reduces the dependence on external infrastructure for some applications.

Cost management reverses adoption policies

Earlier this year, several tech giants urged employees to use AI tools as extensively as possible in a trend referred to as tokenmaxxing, with Meta even stating that it would evaluate employee performance based on usage of AI tools.

Owing to the restrictions and a broader push to streamline AI costs, Meta has now reversed this approach and encouraged staff to be more efficient with AI tokens, which are the units that measure AI usage.

Meta is not alone in adjusting its strategy to manage escalating technology costs as companies reassess their AI deployment strategies in response to infrastructure constraints and rising operational expenses.

In June, Amazon dropped its internal initiative that sought to encourage employees to embrace AI tools after it emerged staff were using AI to complete what Financial Times described as pointless tasks.

The policy reversals could show how technology companies are beginning to prioritise cost efficiency over aggressive adoption as they encounter capacity constraints and evaluate the return on investment from AI deployments across their workforces.