Even the world's biggest technology companies are running into the same problem facing the broader AI industry: there is not enough computing power to meet demand.

According to a report by the Financial Times, Google has restricted Meta's access to its Gemini AI models after the Facebook, Instagram and WhatsApp parent company requested more computing capacity than Google was able to provide. The decision reportedly forced Meta to delay some internal AI projects and rethink how its engineering teams used AI resources.

The development offers a rare glimpse into the infrastructure bottlenecks behind today's AI race, where access to computing power has become as valuable as the AI models themselves.

The AI Capacity Crunch

The report says Google informed Meta around March that it could not provide the full Gemini capacity the company wanted to purchase because of limited available infrastructure.

Meta was reportedly affected more than other Google customers because of the scale of its demand. In response, the company asked employees to be more conservative with their use of AI tokens, the units that measure interactions with AI models and determine how much computing capacity is consumed.

The situation highlights a growing reality across the industry. While companies continue investing billions of dollars in AI chips, cloud infrastructure and data centres, demand for AI computing resources is growing even faster than supply.

Google has previously acknowledged these constraints. During its first-quarter earnings call, Chief Executive Sundar Pichai said computing capacity had become one of the biggest factors limiting the growth of Google Cloud, despite the business generating $20 billion in quarterly revenue.



Why It Matters

Meta is rapidly expanding AI across its ecosystem, from AI assistants inside Facebook, Instagram and WhatsApp to the continued development of its Llama family of large language models. Those ambitions require enormous amounts of computing power to train models, run AI services and support millions of users simultaneously.

The reported restrictions show that even companies building some of the world's most advanced AI systems still depend on external infrastructure providers to meet growing demand.

More broadly, the story reinforces a shift taking place across the AI industry. Competition is no longer centred solely on who builds the best model, but also on who can secure enough GPUs, cloud capacity and data centre infrastructure to keep those models running at scale.

Why Nigeria Should Pay Attention

For Nigeria and the rest of Africa, the development underscores one of the biggest barriers to building competitive AI products: access to compute.

Many African startups, researchers and universities already struggle to access affordable AI infrastructure, making it difficult to train and deploy large-scale models locally. If global technology companies worth trillions of dollars are facing capacity shortages, the challenge is even greater for emerging AI ecosystems.

The growing demand for computing infrastructure also strengthens the case for national and regional investments in AI-ready data centres and shared compute resources, an area that countries such as Nigeria have already begun prioritising through initiatives like the Nigeria AI Scaling Hub.