From Palladium Magazine, July 20:
Over a thousand acres of land in Abilene, Texas, lie flattened with graded earth and poured concrete. The foundation pits are filled with conduit bundles and switchgear, and in some quadrants, fully operational substations have been erected. However, other quadrants sit empty, waiting on a schedule that keeps sliding to the right. Although the supercomputer intended for the site is humming with activity, what registers to a visitor is that the campus is only half-alive.
Abilene was the intended flagship of the Stargate consortium. Announced from the White House days after President Trump’s 2025 inauguration, SoftBank’s Masayoshi Son, Oracle’s Larry Ellison, and OpenAI’s Sam Altman stood beside the president as he declared the project’s ambition to pour $500 billion into a combined data center and supercomputer over a four-year horizon. While this signaled Washington’s blessing on a scale once reserved for projects like the Apollo program, those had the federal government as their underwriter and customer. Stargate would have neither. Private capital alone was set to accelerate machine intelligence through GPUs, land, and power, with the White House only lending its podium.
Today, the grounds of Stargate’s flagship site in Abilene suggest the fruits of this effort. Through the first half of 2026, stalled negotiations shrank the question from when the site would finish to how much power it would ever draw. To the initial 1.2 gigawatts planned—enough power to supply roughly a million homes—Abilene’s Stargate was slated to add nearly another gigawatt. Papers were drawn up and financing was secured, but the deal did not actualize. Originally projected to finish in March 2026, the site is now expected to be fully energized as late as mid next year, and operational and financial issues have canceled its ambitious expansion.
However, it is a mistake to interpret this by simply saying that “Stargate failed,” and the way in which that reading is wrong holds a valuable lesson for the future of the American AI buildout. The main campus still serves OpenAI compute. But the broader buildout that served to scale the multi-gigawatt arrangement between OpenAI, Oracle, and Crusoe collapsed. Abilene leaned heavily on two counterparties and their capacity to shoulder compute infrastructure risk. Stargate attempted to separate out risk according to whoever could best bear each specific burden, but when OpenAI’s internal cash-flow forecasts moved far enough, the deal’s structure could not absorb the swing. This led to OpenAI and Oracle abandoning nearly an extra gigawatt of capacity next to an existing supercomputer.
Is Stargate a failure on a national scale? Not yet. But this example is emblematic of a larger failure that looms on the horizon. The American AI buildout rests unsustainably on the same few corporate treasuries, and it is neither standardized nor repeatable in a manner that lets American capital markets fund frontier compute. This is unlike the way assets ranging from mortgages to traditional power plants are funded. Instead, urgency and a lack of an existing playbook mean capital comes in through side doors.
The question of interest is not whether America has money. By one measure, the United States holds roughly 40 percent of the world’s equity and a comparable share of fixed income. This is the largest concentration of hungry capital ever assembled. So what happens when the conversion machinery decays exactly as we face the buildout that will test it the hardest?
American Capital Can’t Reach Compute
America’s wager has never been laissez-faire in the strict sense. It is that capital markets, properly institutionalized, turn private capital into public goods. The lazy version of the story is that America built its great physical infrastructure by getting out of the way, but this is an incorrect reading.
Historically, America has developed the institutions that let private capital grasp and fund public infrastructure. In the nineteenth century, transcontinental railways were stood up and operated via land grants and public charters, but institutions and assets were developed in tandem. No deep market for industrial securities existed when the first rail promoters, long before Vanderbilt, started laying track. Rail bonds and shares were the instruments on which early American securities markets cut their teeth. Standardized paper, ratings, reporting, and, later, exchanges were developed while tracks were being laid. The funding apparatus that turned a distant, disparate, and initially underfunded project into something that counterparties could confidently fund at arm’s length was called into being by the infrastructure itself.
Thanks to the repetition of this dynamic, the United States possesses the deepest and most liquid capital markets on the Earth across pension funds, insurers, and private credit. The problem is that the instruments and standards that would make AI infrastructure projects bankable do not yet exist. A large portion of this liquid capital is hunting for long-duration and high-demand assets embodied in AI infrastructure. Capital is abundant, but bankable assets are not.
Corporate finance provides funds exclusively based on the balance sheet and expected financials of a company, providing a useful vehicle to raise debt for asset expansion. Project finance, by contrast, funds a predictable, contracted, and long-lived asset, especially one with high capital needs or a public-goods character, such as toll roads, pipelines, and traditional power plants. Project finance does this by walling the asset off into its own entity and lending against its cash flows through non-recourse or limited-recourse debt: because the lenders’ claim runs only to what the asset alone will generate, future revenues serve as both the basis for the loan and the lenders’ only real recourse if things go wrong.
The compute buildout is awkwardly positioned between corporate and project finance because it has the risk profile of the former and the scale of the latter, adding billions of dollars of debt to a company’s balance sheet. As such, there is currently no capital-conversion machinery—no templates—that fit this asset class cleanly. There are a few aspects of its asset dynamics that explain why it is so exotic compared to past infrastructure buildouts....
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