Meta

systems Renting out spare compute is simply the natural progression for any sufficiently large infra company Meta seems to be having a bit of an identity crisis. 2 to 5 gigawatts. The news comes less than a week after a report broke claiming that Meta was actively exploring options to offload its excess compute capacity to other AI labs. So, which is it, Zuck? Did you invest too much or too little in AI? The easy answer is that Meta overcommitted. Inspired by the early success of Llama, it made a huge bet on the AI gold rush. Offloading spare compute to the highest bidder is just a hedge in case its Superintelligence team turns out to be another pipe dream, like the Reality Labs Metaverse that utterly failed to spark enthusiasm for immersive environments accessible through Meta's Quest cybergoggles.

The more pragmatic read is that Zuckerberg has woken up to the fact he’ll never be as cool as OpenAI boss Altman or Anthropic's Amodei, and renting out spare compute is just the natural progression for any sufficiently large hyperscaler. Meta's business model is closer to Google's than those operated by OpenAI and Anthropic. Both Meta and Google offer various services which generate revenues by connecting users with advertisers. For Google it’s a search and entertainment empire. For Meta it's enabling an endless feed of content generated by friends, family, influencers, and yes, bots.

5 billion in profits last year, respectively. That's profit, not revenue. But both are now plowing over $100 billion a year into AI infrastructure to power large language and image and video generation models. As we learned from Meta’s recent earnings calls, the most commercially potent of those models get the right ads in front of the right eyeballs. The open secret is Meta was already one of the most successful AI companies long before ChatGPT debuted. Except, it's not large language models (LLMs) that make Meta money, at least not in the conventional sense.

Instead, Meta’s most profitable AI models are the recommender systems that mine profiles for context and use it to infer your needs. Meta's devs evolved those models considerably over the past few years, and their architectures now look a lot more like an LLM than the now-pedestrian neural networks on which Zuckerberg built his empire. Google is in a similar situation. It’s investing heavily in AI to feed its fast-growing and profitable cloud business, even as advertising still pays most of the bills. But unlike Google, Meta hasn’t yet made the leap from hyperscaler to cloud provider.

Amazon, Google, Microsoft, even Oracle got there eventually, and it seems AI may be the catalyst that turns Meta into a cloud, too. Recent reports suggest that Zuckerberg is warming to the idea. “I think that’s certainly a thing that we could do and that I think would make sense to consider,” he said in an interview with Bloomberg last week. ” But while the demand may be there, Zuckerberg emphasized the compute capacity is not readily available. But as Ben Thompson of Stratechery put it, cashing in on this compute may be more than a backup plan.

In a post channeling an imaginary Zuckerberg, Thompson suggested that becoming a neocloud would force Meta to stop chasing pipe dreams and pet projects. His logic is that if Meta can't make money with infrastructure it buys for AI ventures, the social networking giant can lease the orphaned hardware to the highest bidder. The takeaway for investors — should Meta follow its fellow hyperscalers-turned-cloud-providers down this road — is that the profitability of its hardware investments would no longer be tied to its ability to commercialize them.

If history tells us anything, scale matters. Building a cloud like Amazon Web Services (AWS) is next to impossible unless you've already figured out how to profit from those same resources. Meta's scale puts it in a position to acquire compute in volumes impossible for smaller players. Its ability to capitalize on infrastructure demand relies entirely on having something others want but can’t get anywhere else. For what it’s worth, Zuckerberg wouldn’t be the first to come to this conclusion. Earlier this year Musk-owned xAI surprised many when it announced plans to rent out its Colossus supercluster in Memphis to rival model dev Anthropic.

The calculus here is the same. Making a profit off LLMs, like Grok, isn't easy — just ask OpenAI — but selling the means of AI production to those that haven’t yet figured that out is enormously lucrative. The logic appears to have gotten Zuck's attention. “The SpaceX model I think is quite interesting in terms of just making these short-term deals that are at a big premium,” Zuckerberg told Bloomberg. ” Reports suggest Meta is seriously considering two strategies for commoditizing its compute assets. The first would be a usage-based compute platform similar to Amazon Web Services' Bedrock.

The service would allow customers to run models and serve them through APIs — interfaces that abstract operational complexity.