Is this what losing looks like in the AI wars? You spend more than $140 billion over three years building the machinery to win, then start renting it to the very rivals you meant to beat. On Wednesday, Bloomberg reported that Meta is building a cloud business, under its internal Meta Compute initiative, to sell access to its AI computing power and models. The plan comes in two forms. Meta would let developers pay to use its models hosted on its own infrastructure, including the closed-weight Muse Spark, in the style of Amazon's Bedrock. And it would rent out raw compute capacity like a neocloud such as CoreWeave. In doing so it goes up against AWS, Azure and Google Cloud, the incumbents who actually run this business at a profit.
Meta is the one hyperscaler that never became a cloud provider, because it always consumed every chip it could buy to power its own products. Now it wants to sell that capacity to outsiders. When you build a fleet to train frontier models and then lease the fleet to others, you are telling the market you have more silicon than you have worthwhile internal solutions to run on it.
This is reminiscent of xAI. Rather than becoming a leading model developer, Elon Musk's outfit, now folded into SpaceX, rents its Colossus data centres to others. In May, Anthropic took more than 300 megawatts of that capacity, over 220,000 Nvidia GPUs. In June, Google agreed to pay SpaceX around $920 million a month for xAI compute through 2029. Yann LeCun, Meta's former chief AI scientist, has called xAI "kind of a failure", its founding team gone and compute rental the only way left to recoup the cost of the hardware. His old employer is now walking the same road.
The people problem tells the same story as the hardware. Inside Meta, morale in the 6,500-strong Applied AI unit has cratered. Wired described a staff livestream hijacked so someone could demand a senior executive be told he was "a piece of shit". TechCrunch summed up its engineers' verdict as a "soul-crushing gulag". More than 1,600 employees signed a petition against a scheme monitoring their keystrokes for training data. Zuckerberg has acknowledged the "distress" caused, after roughly 8,000 job cuts in May and 600 losses from Superintelligence Labs last October.
The defections started early. Within two months of the new lab launching last summer, at least eight researchers walked. Ethan Knight and Avi Verma each returned to OpenAI after less than a month. Chaya Nayak, a nine-year Meta veteran, left for OpenAI. Bert Maher, a twelve-year veteran, went to Anthropic. Meta's line is that "some attrition is normal".
It is a negative picture, but we've yet to see if Meta's lab can deliver. On the same day the cloud story broke, AI chief Alexandr Wang told staff that Meta's next model, codenamed Watermelon, has caught up with OpenAI's GPT-5.5 on key benchmarks and uses ten times the compute of Muse Spark. That is exactly the kind of workload that would fill the excess capacity Meta is now trying to rent out. When a user asked Wang about a Claude Opus-level coding model, he replied "pretty soon", promising users would like what Meta has cooking. The elite team behind Watermelon has been shielded from the layoffs and most of the exits.
Journalist M.G. Siegler argues the cloud move is the obvious fix for a problem Meta has always had. Unlike Amazon, Google and Microsoft, Meta has no way to monetise AI directly, only through ads. A cloud business solves that. He points to Google, once dismissed as a one-trick ads pony, whose cloud arm now runs at over $80 billion a year and is growing above 60%. Wall Street agrees with him, at least for now. Meta jumped 9% on the news, its best day in months, and Mizuho called it a "margin of safety", the thing that eases the biggest overhang on the stock.
Both the failure camp and the Siegler camp agree on the underlying fact: Meta and xAI built enormous compute without the internal demand or model sophistication to use it.
Meta is the one member of the Magnificent Seven without a native answer to how AI pays for itself. Nvidia sells the silicon. Amazon, Microsoft and Google rent compute through clouds that were already businesses. Google adds custom chips and consumer monetisation on top. Apple needs AI to sell its hardware. Meta has none of that. It builds like a hyperscaler, spends like OpenAI, and earns like an ad company, and those three identities do not yet cohere. Its 2026 capex guidance, nearly double last year's spend, is among the biggest proportional leaps in the group, with the least obvious payoff. The Mag 7 trade is splitting apart as AI spending separates winners from laggards, and Meta sits right on the fault line, too committed to be cautious like Apple, too short of direct monetisation to be safe like Google.
Takeaways: A Meta compute business would benefit almost everyone outside the current duopoly. The frontier today is effectively OpenAI and Anthropic, with Google losing ground fast, and that concentration sets the prices, the rate limits and the terms everyone else builds on. There is a natural ratio between compute spent training models and compute spent serving them, and Meta and xAI ended up stuck at the wrong end of it, pouring silicon into training models that then drew too little user demand to earn their keep on the other side. Renting the fleet out is how you rebalance the books when your own models cannot. A Meta that rents out that capacity, ships Watermelon at GPT-5.5 level, and reopens even part of its old open-weight instinct would put cheap compute and a fourth serious model into the market at once. That is more choice for developers, downward pressure on token prices, and less reliance on two labs. Meta's worst year as an AI company could turn out to be a good one for those looking for compute and choice.