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....
