{"slug": "commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals", "title": "Commodification of Intelligence: Good, Bad, and Ugly Circular AI Deals", "summary": "Circular AI deals, such as OpenAI raising money from Microsoft and spending it on Microsoft servers, or Nvidia backing CoreWeave debt for GPU purchases, are not signs of a bubble but rather indicate the commodification of intelligence, according to an analysis. These deals mirror traditional commodity infrastructure financing, where guaranteed off-take agreements enable large-scale development, and suggest AI compute is becoming a fungible resource like electricity or oil. However, the analysis warns that some circular deals that do not follow this commodity analogy introduce risks that could lead to future surprises for companies, investors, or the sector.", "body_md": "Every few months, and especially this week, the AI space gets criticized for circular financing and customer relationships. OpenAI raises money from Microsoft, spending it on Microsoft servers; Nvidia backstops CoreWeave debt, and CoreWeave buys Nvidia GPUs. “The bubble is about to burst!” analysts scream.\n\nAnalysts point to dot-com deals with circular investments in 1999, arguing that this is all bound to\nhappen again[[1]](#ref-1) [2]. They are wrong.\n\nCircular deals are more interesting than “good” or “bad.” They show the AI industry isn't just maturing, but modifying the idea of “AI” to something that is less a technology product, and more a commodity. Imagine that—intelligence available like electricity, and the underlying financial system structured accordingly.\n\nThe evolution of circular deals points to the *commodification* of AI, where\ncompute capacity is moving from a business model where you buy a product (e.g., the hardware, or\nspace in a data center) to something so fungible that you buy it the way you buy electricity,\ncopper, natural gas, or other commodities.\n\nWe'll explore why circular deals are particularly important in commodity industries and\nwhat this implies for understanding the trajectory of AI. First we'll cover how major investments in *traditional* commodities\nmarkets work to ground our analogy more clearly. Next, we'll cover the investments that\nmimic this process in AI, showing how such investments can be healthy. Finally, we'll\nexplore a few examples where circular deals do *not* abide by this analogy and how these\ndeals are introducing risks that could one day turn into awful surprises for the companies\nthemselves, their investors, or the entire sector.\n\nComplex commodity infrastructure like mines, refineries, and ports comes with such large development costs that a bank lending a development company money can potentially risk its own solvency in doing so. Circular deals with multiple customers or beneficiaries, and potentially even governments, are often the only solution that gets shovels in dirt or ships in the water.\n\nLet's look at a simplified example of such a deal. Imagine you want to develop your region's economy with several wells and a pipeline, and you can't afford it. You partner with a commodity trading firm who agrees to make your company attractive to banks or bond buyers by guaranteeing they will buy all your oil at a certain price. This means you are guaranteed revenue for the foreseeable future, and the banks know they can trust you'll repay their loans. The trading firm might even take an equity stake in your company for good measure—potentially to encourage better governance or oversight.\n\nWith such a relationship between you and the oil trader, you've got oil, a large bank loan, and a guaranteed customer… you've got yourself a circular commodities deal!\n\nThis is not a contrived example; it's a common strategy developed and evolved since the\n1960s. Japanese commodities traders and development banks financed infrastructure to enable\ncommodity development, committing to future purchases and equity deals [3]. Jamaica did so in the 1980s\n\nMore recently, the US government began facilitating circular deal making to stimulate the critical\nminerals sector in its bid for supply chain resilience. Last year, MP Materials, a relative\nnewcomer to magnet manufacturing and critical minerals, announced a 10-year relationship with the\nDepartment of War, where the latter committed to buying all neodymium-praseodymium (i.e., magnets)\nfrom the company for at least $110/kg [5]. Since then,\nsuch off-take agreements have been announced between MP Materials and General Motors\n\nFungible commodities with large global markets are particularly well suited to such deals because the counterparty guaranteeing to be a customer (i.e., the oil trading firm in our example above) knows there is a large market they can tap into. They likely have a history of successfully making such sales, otherwise they wouldn't have billions of dollars and a pristine reputation they can leverage.\n\nThe frontier generative AI industry—be it model development or inference—is very much dependent on Nvidia. GPUs are effectively a fungible commodity thanks to Nvidia's development of the underlying infrastructure and standardization across all firms in the space. Three forces are enabling AI chips and associated data centers to act like a fungible commodity:\n\nAs a result, GPUs, electricity, and data centers are all effectively fungible, with large order backlogs and pent-up demand. If you build a data center and can't leverage it for your own business, there's a good chance you can sell it to someone who can—they might even pay a premium for availability in the short term.\n\nSpaceX and Meta, despite trying to build top-tier foundation models, are profitably leasing their\nown data centers to others—these data centers are working today and ready for frontier lab\nworkloads. Well-capitalized labs are willing to pay a huge premium for the privilege. SpaceX\nleases its Colossus 1 and 2 data centers for over $2 billion per month [8], at a significant markup over other smaller clouds and data\ncenters\n\nLet's now return to the aspiring startup or neocloud. You are starting up and, like our oil example earlier, have proven yourself on a small scale but now need your own data center or access to thousands of GPUs. What can you do?\n\nEnter Nvidia and the circular deal—much like the oil deal.\n\nNvidia provides the capital and product access to your startup, prioritizing your access to its GPUs\nso you can get the hardware you need. Nvidia has a $1 trillion order backlog [10] and knows it can resell your hardware or make better use of\nit if you fail, so it goes a step further: it becomes the guaranteed buyer of your compute if you\ncan't take advantage of it… much like SpaceX and Meta above. This is not\ntheoretical—SemiAnalysis provides estimates for Nvidia compute off-take agreements and\npricing\n\nThis is, of course, one type of circular deal, and a relatively simple one at that. It's been\nused in CoreWeave's $6.3 billion deal with Nvidia [12], alongside smaller data center operators like Firmus ($505\nmillion\n\nThis helps explain why we see so many interconnecting and circular\nrelationships—OpenAI cancels its deal with Oracle, so Meta swoops in [14]; SpaceX leases servers to Google, Google invests in\nAnthropic; and so on.\n\nWall Street gets circular deals, and humanity gets artificial general intelligence… purportedly.\n\nNvidia argues it is supporting a global AI ecosystem. Unfortunately, this doesn't preclude it\nfrom overextending itself. A $6.3 billion deal with CoreWeave is one thing, but committing up to\n$750 billion [2] is another.\n\nThe success of this approach for startups and neoclouds also assumes that AI and data centers continue to be fungible and “resellable”. Should standards or chipsets change, or should technologies make it easier to run local models or models on small clusters, then the business models might fail and the off-take agreements Nvidia has might not help the ecosystem much.\n\nCircularity becomes particularly nefarious when it is used to hide the effects of the investments, debts, or other obligations from investors.\n\n[Figure 1](#fig-google-backstop) shows the FT's [15] breakdown of a recent TeraWulf bond deal. In this case,\nTeraWulf can obtain financing given Google's backstop of any lease failures, should Fluidstack\nnot be able to pay TeraWulf, or Anthropic unable to pay Fluidstack\n\nSimilarly, Meta's $27.3 billion Hyperion data center bond sale is an off-balance sheet one [17], as is its more recent $12.3 billion deal marketed\nby BlackRock\n\nMany finance professionals argue that these bonds are ultimately guaranteed by the impressive and continually growing revenues and profits from the hyperscalers, so there is nothing to worry about. The reason these bonds find so many customers, despite the circularity label, is that the final guarantors (i.e., the hyperscalers) generate billions of dollars of profit every year and can easily cover these costs, should it come down to that.\n\n… but this is *today*, and it's with the current bond deals. Will\ntomorrow's bond deals be supported by the hyperscalers in the same way? And what happens if\nhyperscaler revenue trends change? What if the banks begin expanding the backstop agreements from\nhyperscalers to “generally pretty decent” companies? What if the backstop fails to be\nenforced? Will we see hyperscaler-backstop-backed-bonds grouped together, collateralized, and\nresold the way Mortgage Backed Securities were in 2007?[[19]](#ref-19)\n\nCircular deals are not bad. In fact, they are critical in the development of the AI ecosystem much like such deals are used in critical minerals, oil, electric vehicles, and other capital intensive industries with incredibly high startup costs.\n\nThe circularity helps illustrate the commodification of AI today, and how the technology might one day be more like electricity or an internet connection, rather than a product one buys or subscribes to.\n\nWhen circular deals are supported by overextended lenders, or when overextended lenders try to move\nsuch deals off their balance sheets, they become ugly and bad—in other words,\nincredibly risky. This industry is likely to grow much more in the coming years, so it is important to watch for the lowering of standards or aggregation of risk. As SemiAnalysis\nwrites [11]:\n\nAI Debt Financing will become a multi-trillion-dollar credit market, with over $7T of debt outstanding by 2029 driven both by AI IT Capex and AI Datacenter Capex needs [...] This will make it the second largest asset backed debt market after the US mortgage-backed financing market at just over $13T.\n\nIn this evolution, there might be a few bad deals along the way. It's important to keep your eyes open.\n\nGet notified when we publish new analysis on AI, geopolitics, risk, and more. We will never share your e-mail with anyone.", "url": "https://wpnews.pro/news/commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals", "canonical_source": "https://www.emergingtrajectories.com/lh/commodification-and-circularity/", "published_at": "2026-07-29 18:57:10+00:00", "updated_at": "2026-07-29 19:03:58.715929+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-ethics"], "entities": ["OpenAI", "Microsoft", "Nvidia", "CoreWeave", "MP Materials", "General Motors", "Department of War"], "alternates": {"html": "https://wpnews.pro/news/commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals", "markdown": "https://wpnews.pro/news/commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals.md", "text": "https://wpnews.pro/news/commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals.txt", "jsonld": "https://wpnews.pro/news/commodification-of-intelligence-good-bad-and-ugly-circular-ai-deals.jsonld"}}