Meta Starts Renting Out Its AI Power: When Everyone's Digging, the Shovel Seller Profits - If the Bubble Doesn't Burst First

Meta is building a business to rent out its spare AI computing power - stepping straight into the yard of Amazon Web Services, Google Cloud, and Microsoft Azure. The plan, reportedly called Meta Compute, comes just weeks after SpaceX did the same: its xAI division already leased capacity from the Colossus 1 data center to companies including Anthropic, Google, and Reflection AI.

The logic is as old as any gold rush. When everyone's digging, the surest money goes to whoever sells the shovels. Among the giants spending hundreds of billions on chips and electricity, the temptation arises to keep part of that expensive infrastructure from sitting idle, and instead have it bring in cash from others who don't own their own servers.

The number behind this is hard to grasp: by the first quarter of 2026, Meta committed to spending 182.9 billion dollars (around 168 billion euros) on AI infrastructure, with enormous data centers in Louisiana and Ohio, the Ohio one expected to come online this year. When you invest that much, you have to find a way to make the money come back - and renting out the power is a faster route than waiting for your own models to earn.

And here's the weak spot. Unlike Google and OpenAI, Meta hasn't so far pulled serious revenue from its own AI products; in its financial reports it doesn't separately state how much Meta AI or Llama bring in. Now the company wants to sell both raw computing power and access to models, including the recently launched closed model Muse Spark. In other words, instead of waiting for customers, it charges those building its foundations.

Analysts are already whispering the word everyone in the Balkans recognizes - bubble. Some warn that this whole build-out rests on hardware that ages at an incredible pace, and the question no one wants to answer out loud is whether AI companies will pull enough revenue from end users at all to justify investments in the trillions. When infrastructure is built faster than the revenue meant to pay for it, someone ends up holding the bill. The only question is who.