Google Caps Meta’s Gemini Access as $920 Million SpaceX Deal Fails to Ease AI Strain
Updated
Updated · Financial Times · Jun 28
Google Caps Meta’s Gemini Access as $920 Million SpaceX Deal Fails to Ease AI Strain
1 articles · Updated · Financial Times · Jun 28
Summary
Google told Meta around March it could not supply all the Gemini capacity the company wanted, and the limits remain in place, disrupting and delaying some of Meta’s internal AI projects.
Meta’s unusually heavy use of Gemini made it one of the hardest-hit customers, prompting staff to conserve AI tokens while the company also pushes to rein in AI spending.
Google’s cap highlights a broader compute shortage: despite tens of billions in chip and data-center spending, demand for inference and other AI workloads is outpacing available capacity.
A $920 million-a-month SpaceX leasing deal underscores Google’s scramble for more compute, after Sundar Pichai said cloud revenue topped $20 billion but would have been higher without constraints.
The squeeze also exposes Meta’s reliance on rival models even as it invests $600 billion in US infrastructure by 2028 and shifts some workloads toward its newer Muse Spark model.
Was Google's AI cap on Meta a resource crisis, or a strategic move to hobble a key competitor?
Is the AI boom hitting a physical wall, limited not by code but by power grids and electrical parts?
$320 Billion AI Compute Crunch: How Google, Meta, and SpaceX Are Racing to Overcome the 2026 Infrastructure Bottleneck
Overview
In March 2026, Google informed Meta that it could not meet Meta’s massive demand for Gemini AI computing capacity, leading to a usage cap. This was because Meta’s requirements were much heavier than other Google Cloud customers, making Meta the most affected by these constraints. As a result, Meta’s internal AI development slowed down, with several projects delayed. To cope, Meta teams had to use tokens more carefully to manage limited resources. This situation highlights how growing demand for AI compute is straining even the largest tech companies, forcing operational changes and revealing the challenges of scaling AI infrastructure.