Nvidia May Guarantee $250 Billion So OpenAI Can Rent a Data Center: Hassan Taher’s Assessment

13th Aug 2026
The most important AI story of the past week has nothing to do with model capability. Over July 26 and 27, the Wall Street Journal and Bloomberg reported that Nvidia is in talks to guarantee roughly $250 billion in financing so OpenAI can lease a 10-gigawatt data center campus in southern Ohio. A separate discussion reportedly covers up to $350 billion to help finance OpenAI's purchase of Nvidia chips to fill it. The market reaction told you what the market thought. Nvidia shares fell 4.5 percent in late morning trading. More tellingly, credit default swaps on Nvidia bonds — essentially default insurance for bondholders — recorded their sharpest intraday jump since they began actively trading in November. When the debt market gets nervous about the most profitable company in the industry, the nervousness is worth understanding. What the Structure Actually Does The Ohio project is being developed by an arm of SoftBank on a decommissioned uranium enrichment site on federal land, roughly seventy miles south of Columbus. Power will come from a new $33 billion natural gas generation build-out funded by Japan, with the Commerce Department holding control over the supply. Including chips, the total cost could exceed $500 billion, placing it among the largest data center projects ever announced. The reason a guarantee is needed at all is the part worth sitting with. OpenAI is not profitable and cannot qualify for investment-grade credit on its own. It is the same weakness that will shape its reception as a public company, a dynamic Hassan Taher has traced in the questions public markets are likely to put to AI labs as Anthropic and OpenAI move toward listing. Without a backstop, the developer would be lending against the promise of a company with enormous revenue growth and no earnings. With Nvidia's name attached, the developer can raise debt on far more favorable terms. For OpenAI, the deal would produce its first directly leased data center and reduce its dependence on Microsoft, Amazon, and Oracle for infrastructure. For Nvidia — market capitalization hovering near $5 trillion, gross margin around 75 percent — it locks in years of chip demand. Nvidia announced a second deal the same day, investing in and partnering with Ilya Sutskever's Safe Superintelligence, saying it had obtained "rare access into the company's closely guarded research" and would help the startup increase its compute by an order of magnitude. Representatives for both OpenAI and Nvidia declined to comment on the reported data center talks. The Word Everyone Reached For Analysts converged on the same term within hours: circular financing. The pattern is straightforward enough to describe in one sentence. The chip supplier finances the customer, the customer buys chips from the supplier, and the supplier books the revenue. Nvidia has equity or financing relationships across a substantial share of its own demand base, including AI labs and data center operators. Critics argue this arrangement can make demand look more robust than end-user economics actually justify. Hassan Taher, an AI analyst and author who advises organizations on enterprise AI strategy, has argued that the financing architecture of the buildout deserves at least as much scrutiny as the technology itself. "There is a version of this deal that is entirely rational, and a version that is a bubble mechanism, and the two look almost identical from the outside," he has noted. "The rational version is a supplier with an unusually clear view of future demand using its balance sheet to unblock capacity it knows will be consumed. The bubble version is a supplier manufacturing the demand it then reports as revenue. The difference is not in the structure. It's in whether the underlying end customers actually generate enough cash to service the debt." The historical comparison analysts keep raising is Lucent in the late-1990s telecom boom. Lucent lent billions to telecom carriers so those carriers could buy Lucent equipment. Demand looked spectacular while the loans were being written. When the carriers failed after the dot-com collapse, Lucent absorbed enormous write-downs on receivables from customers that no longer existed. The parallel is imperfect — Nvidia's customers include some of the best-capitalized companies on earth, and its cash generation dwarfs anything Lucent had — but the structural resemblance is real enough that credit markets priced it. Why the Interconnection Is the Actual Risk The systemic concern is less about any single transaction than about how tightly the participants are now bound together. Nvidia, SoftBank, the hyperscalers, and a growing set of specialized data center operators are linked through equity stakes, leases, bridge financing, and guarantees. In a rising market, that interconnection amplifies growth. In a downturn, it transmits stress. A slowdown in AI adoption would not affect one company; it would move through a network of counterparties who are each other's revenue, each other's creditors, and each other's collateral. Taher has cautioned against reading either the enthusiasm or the alarm too literally. "Every large infrastructure cycle in history has been financed by somebody who was also selling into it — railroads, telecom, fiber," he has observed. "Sometimes the capacity got used and the financing looked visionary. Sometimes it didn't and the financing looked reckless. The honest position right now is that we don't yet know which one this is, because we don't have five years of inference demand data at this scale. What we do know is that the leverage is real, the counterparties are concentrated, and the assets have a depreciation schedule measured in a handful of years, not decades." That last point is the one most easily overlooked. A rail line lasts a century. A GPU cluster has a useful competitive life measured in a few years before the next architecture makes it economically obsolete. Debt taken against fast-depreciating assets requires the revenue to arrive quickly, which puts unusual pressure on AI adoption curves to steepen on schedule. The costs that never appear on a price sheet — power, cooling, replacement cycles, and the operational overhead of running these systems at scale — are the subject of Hassan Taher's accounting of AI's hidden $25 billion tab, and they are precisely the costs a lease guarantee cannot make disappear. What Businesses Should Take From It For companies buying AI rather than building it, none of this changes next quarter's roadmap. But it does argue for a specific kind of caution: avoid architecting critical workflows around assumptions of permanently cheap inference or permanently available capacity. Pricing in this market is currently shaped as much by financing conditions and strategic subsidy as by underlying cost. Both can move. The deeper significance of the Ohio deal is what it reveals about where the constraint now sits. The bottleneck in AI is no longer ideas or even chips. It is power, land, and — increasingly — the willingness of someone with an investment-grade balance sheet to stand behind the debt. When a chipmaker becomes the credit backstop for its largest customer's real estate, the industry has entered a phase that looks less like software and more like heavy industry. That transition tends to be where the most consequential financial mistakes get made. This article discusses corporate financing arrangements and market reactions for informational purposes. It is general commentary, not investment advice; readers considering investment decisions should consult a qualified financial advisor. Sources: Nvidia reignites "circular" financing concerns as it weighs OpenAI deal — Axios Nvidia in talks with OpenAI to guarantee $250 billion financing for data center — The Wall Street Journal Nvidia's $750 Billion Deals Revive Fear of AI Circular Financing — Bloomberg Nvidia weighs $250 billion guarantee so OpenAI can lease SoftBank's 10-gigawatt Ohio campus — Tom's Hardware Nvidia's Potential $250 Billion OpenAI Financing Deal Is Reviving a 1990s Tech Bubble Habit — Benzinga