AI Giants, Up to Their Necks in Debt, Start Remaking America

The relentless West Texas dust, a fine, iron-tinged grit, coats everything in Abilene, a constant, dry reminder of this stark landscape. It’s here, amidst this arid expanse, that OpenAI CEO Sam Altman is orchestrating “Stargate” – a sprawling, rapidly expanding network of AI data centers. This ambitious project, backed by tech titans like Oracle, Nvidia, and SoftBank, represents a monumental leap in AI infrastructure, demanding an unprecedented scale of investment and resources.

The sheer human effort involved is staggering. Six thousand workers’ vehicles converge on the site daily, their tires churning up a perpetual haze of dust over a construction footprint that rivals a small city. This single campus alone employs more people than OpenAI’s entire global workforce. The region’s volatile weather mirrors the intensity of the construction: sudden downpours turn the parched earth into thick, clinging mud, only for the sun to bake it back into a cracked, chalky surface as the storms pass. As dusk settles, the dramatic desert sky, stripped of shorter wavelengths, ignites in fiery reds and oranges, a fitting backdrop to the transformative work underway.

“This is what it takes to deliver AI,” Altman stated, his gaze fixed on the horizon during a recent visit. “Unlike previous technological revolutions or versions of the internet, there’s so much infrastructure required. And this is a small sample of it.”

“A small sample” is an understatement. With each Stargate site estimated to cost around $50 billion, the total investment in OpenAI’s data center projects is projected to reach a staggering $850 billion. This figure alone accounts for nearly half of the $2 trillion global surge in AI infrastructure spending forecast by HSBC. The Abilene campus already boasts one operational data center, with a second nearing completion. OpenAI CFO Sarah Friar indicated that the site could eventually scale to over a gigawatt of power capacity, enough to energize approximately 750,000 homes – roughly the combined population of Seattle and San Francisco.

“The shovels going into the ground today are building compute power that will come online in 2026,” Friar explained. “The initial Nvidia push will be for their new Vera Rubin accelerator chips. But the real focus is on what’s built for ’27, ’28, and ’29. We are currently facing a massive compute crunch.”

Altman echoed this urgency: “We are growing faster than any business I’ve ever heard of before. And we would be much larger now if we had significantly more capacity.” The confluence of readily available land, accommodating local governments, and a grid infrastructure, for now, amenable to expansion, has created a fertile ground for these AI kingdoms.

**The Titans of Compute: Zuckerberg, Musk, and the AI Arms Race**

Altman’s Stargate is not an isolated endeavor. Across the nation, tech giants are engaged in a colossal build-out of AI infrastructure, reshaping the country’s industrial and technological landscape.

In the flatlands of northeast Louisiana, where soybean fields once dominated, Meta’s Mark Zuckerberg is constructing Hyperion, a four-million-square-foot monument to artificial intelligence. Upon completion, this facility is expected to consume more electricity than the city of New Orleans and occupy a footprint comparable to lower Manhattan.

Across the Mississippi River in West Memphis, Arkansas, Alphabet’s Google is developing what state officials describe as the largest private capital investment in the state’s history: a multibillion-dollar campus sprawling across 1,100 acres of scrubland.

Further south, in South Memphis, Tennessee, Elon Musk has rapidly transformed industrial wastelands with his supercomputer, Colossus. Initially built in a mere 122 days within a defunct Electrolux factory, Colossus 2 is now underway, aiming for a million GPUs. Musk has also acquired a third building to expand the complex and purchased a shuttered power plant in Southaven, Mississippi, to ensure sufficient energy supply.

In southeastern Wisconsin, Microsoft is investing over $7 billion in what CEO Satya Nadella calls “the world’s most powerful” AI data center, slated to house hundreds of thousands of Nvidia chips by early 2026. Meanwhile, in rural Indiana, Amazon has converted 1,200 acres of farmland into “Project Rainier,” an $11 billion facility powered by custom silicon, specifically designed to train AI models for Anthropic.

“Cornfields to data centers, almost overnight,” observed Amazon Web Services CEO Matt Garman. This AI boom is physically manifesting as steel, gravel, and immense power consumption, carving the nation into new zones of technological influence. It represents a profound belief that intelligence itself can be manufactured at an industrial scale, and that the largest “factory” will ultimately prevail.

“This is the largest market in the history of mankind,” stated Sameer Dholakia, a partner at Bessemer Venture Partners. “This is larger than oil, because everyone on the planet needs intelligence.”

**The Financial Undertow: Debt, Demand, and Diluted Valuations**

The financial scale of this AI infrastructure build-out is almost beyond comprehension. The top five hyperscalers—Amazon, Microsoft, Alphabet, and Meta—are on track to spend approximately $443 billion on capital expenditures this year. CreditSights projects this figure to climb to $602 billion in 2026, with an estimated 75% of that spending directly allocated to AI infrastructure.

While the current tech industry is exceptionally profitable, the immense capital required for AI buildouts is straining even the largest balance sheets. Hyperscalers have issued a staggering $121 billion in new debt this year, more than four times the average annual issuance over the previous five years, according to Bank of America. Over $90 billion of this debt was raised in the last three months alone. Meta secured $30 billion, Alphabet raised $25 billion, and Oracle completed an $18 billion bond sale, now ranking as the largest issuer of investment-grade debt among non-financial U.S. companies.

Wall Street anticipates this borrowing spree to intensify. Analysts at Morgan Stanley and JPMorgan estimate that the AI infrastructure push could drive up to $1.5 trillion in additional borrowing by tech companies in the coming years, with UBS forecasting as much as $900 billion in new issuance for 2026 alone.

This unprecedented reliance on debt is creating unease among credit investors. Daniel Sorid, head of U.S. investment grade credit strategy at Citi, noted the “inherent discomfort” associated with such a capital-intensive transformation. This apprehension is reflected in the derivatives market, where credit-default swaps—a form of insurance against borrower default—have widened to multi-year highs for companies like Oracle. Major financial institutions have advised clients to seek protection, and a liquid CDS market for Meta has recently begun trading as investors hedge against the burgeoning hyperscaler debt.

History offers a cautionary tale: the dot-com era saw telecoms over-leveraging to build fiber networks, many of which eventually restructured after market conditions tightened. While the network infrastructure endured, early investors often faced significant losses or complete equity wipeouts.

**OpenAI’s Strategic Nexus: A Web of Interlocking Deals**

At the epicenter of this AI infrastructure race lies OpenAI, whose recent series of strategic partnerships has fundamentally altered the competitive landscape. In a mere two months this past fall, the company announced commitments totaling approximately $1.4 trillion in headline value. These agreements, while ambitious, have fueled concerns about an AI bubble and raised critical questions about the availability of power, land, and supply chains to meet such grand ambitions.

The partnership announcements came in rapid succession:

* In September, OpenAI revealed a $100 billion equity-and-supply agreement with **Nvidia**, granting the chip giant an ownership stake in exchange for 10 gigawatts of its next-generation systems.
* In October, OpenAI partnered with **AMD** to deploy its Instinct GPUs, with the agreement potentially offering OpenAI a 10% stake in the chipmaker.
* Shortly thereafter, **Broadcom** agreed to supply 10 gigawatts of custom chips co-designed with OpenAI.
* In November, OpenAI finalized its first major cloud contract with **Amazon Web Services**, signaling a diversification beyond its previously exclusive relationship with Microsoft.

“We have to do this,” stated OpenAI President Greg Brockman, emphasizing the critical need to secure the raw computing power essential for the company’s mission to scale AI globally.

This intricate web of deals has led some critics to characterize it as a “circular economy.” Nvidia, in essence, finances demand for its own chips; Oracle constructs the physical data centers; AMD and Broadcom emerge as alternative suppliers; and OpenAI acts as the primary demand anchor. This model thrives on sustained growth, but a downturn in demand or a tightening of funding could rapidly propagate stress through these interconnected financial exposures. Nvidia itself has cautioned investors that there is “no assurance” of a definitive agreement with OpenAI or that the investment will be completed on expected terms, underscoring the preliminary nature of many such headline AI pacts.

Oracle, however, maintains a more optimistic perspective, viewing the demand as genuine and broadly distributed. Clay Magouyrk, Oracle’s newly appointed co-CEO, expressed confidence in the existing demand: “We see broad-based demand across a huge swath of the industry, so it’s not just from any one individual place. I don’t worry about a bubble, because I see committed demand for it.” He described the appetite for compute as “near-infinite,” provided the technological enablement is in place.

Anthropic CEO Dario Amodei highlighted the inherent challenges of long lead times in AI development, referring to a “cone of uncertainty” where market dynamics can shift rapidly within a quarter, while data center construction takes 18 to 24 months and chip orders are placed years in advance. He noted that financing for such massive projects often involves partnerships with chipmakers or cloud providers, enabling a “pay as you go” model. Amodei cautioned that while Anthropic strives for discipline, some players may not be managing this risk effectively.

**The Gospel of Scale and the Looming Reckoning**

Skepticism persists regarding the firm commitment behind these headline figures. Gil Luria, a technology analyst at D.A. Davidson, points to Oracle’s situation as a case in point. He suggests that OpenAI’s initial commitments, which Oracle reportedly factored into its financial reporting, may have been more aspirational than contractual. Oracle’s stock experienced a significant drop in November, its worst monthly performance since 2001, partly attributed to these concerns.

OpenAI’s CFO, Sarah Friar, however, rejected the “circular economy” narrative, drawing a parallel to the early days of the internet. “When the internet was getting started, people kept feeling like, ‘Oh, we’re overbuilding, there’s too much.’ And look where we are today, right? The internet is ubiquitous. AI is going to be like that.”

Acknowledging that equity financing is currently too expensive, OpenAI is preparing to take on debt for its expansion. The company has reportedly evaluated over 800 potential sites across North America, scrutinizing factors such as land availability, substation capacity, and transmission infrastructure. Like many in the industry, OpenAI is exploring all viable power sources—renewables, natural gas, and even nuclear—as utilities and tech firms grapple with the need for consistent power, which intermittent sources like wind and solar cannot reliably provide.

“The real bottleneck isn’t money,” Friar emphasized. “It’s power.”

The demand for this power continues to escalate. In late December, SoftBank’s Masayoshi Son agreed to acquire DigitalBridge, a data center investment firm, for $4 billion. To fund this acquisition and his $40 billion commitment to OpenAI, Son divested SoftBank’s entire stake in Nvidia, an action he described as emotionally taxing.

The scarcity of readily available, energized real estate presents a significant hurdle. The expansion of data centers is intrinsically linked to regulatory processes and permits, particularly concerning power infrastructure. OpenAI has lobbied the U.S. government to expand tax credits for AI data centers, though a suggestion for government “backstops” for infrastructure loans was quickly retracted following public backlash. Altman himself has publicly stated that OpenAI “does not have or want government guarantees.”

Undeterred, companies are forging ahead, leveraging debt and capital to build, predicated on the conviction that economic realities will align with their scaling ambitions. This belief is rooted in the observed pattern that increased compute power consistently yields more capable AI models. This principle underpins the astronomical valuations of even unprofitable AI startups.

The wager extends beyond the advancement of AI models through larger training datasets; it encompasses the practical application of these models across various economic sectors—from customer service and code generation to financial analysis and contract drafting. This “inference” phase, the everyday usage of AI, is where the substantial investment must translate into tangible revenue. Each new user, workflow, or agent contributes to a continuous demand for compute power, shifting the build-out from a speculative “moonshot” to a critical utility race.

“We have continued to be surprised, even as the people who pioneered this belief in scaling laws,” remarked Daniela Amodei, Anthropic’s president and co-founder. “Every year we’ve been like, ‘Well, this can’t possibly be the case that things will continue on the exponential,’ and then every year, it has.” Anthropic’s revenue has surged tenfold year-over-year for the past three years, with its valuation skyrocketing from $60 billion to a potential over $300 billion in its current funding round.

**The Reckoning: Genius, Disruption, and the Ultimate Test**

Dario Amodei envisions a future where AI systems, functioning as “a country of geniuses,” possess Nobel laureate-level capabilities across all disciplines, a threshold he believes could be reached as soon as next year. However, he also sounds a note of caution regarding the potential for widespread disruption. “Look at entry-level consultants, lawyers, financial professionals, many of the white-collar service industries; a lot of what they do, AI models are already quite good at without intervention,” he stated, warning that this impact could be “broad, and it’ll be faster than what we’ve seen with previous technology.”

This anticipation is fueling the industry’s spending spree, but skeptics fear a potential debt-fueled overreach leading to bankruptcies and asset fire sales. Matt Murphy, a venture capitalist and early Anthropic investor, views this era differently: “I’ve been in the venture business for 25 years. I’ve seen the cloud wave, the mobile wave, the semiconductor wave. This is the mother of all waves.”

From a distance, a new geography of AI power emerges: Zuckerberg’s Hyperion, Musk’s Colossus, Altman’s Stargate, Amazon’s Rainier, and Google’s sprawling network of compute clusters. Each represents a distinct vision for the future, united by the fundamental constraint of power. Data centers are strategically located near power generation and transmission lines, in areas with affordable land, supportive local governments, and grid infrastructure capable of expansion. These towns are now prominently featured in investor presentations and financial projections.

Analysts suggest the stakes extend beyond stock prices. This period could either mark the dawn of a transformation as profound as electrification and the internet, or it could represent the zenith of a bubble, serving as a historical cautionary tale.

Altman acknowledges the prevailing doubts but remains steadfast in his conviction: “People will get burned on overinvesting. And people also get burned on underinvesting and not having enough capacity.” He anticipates that while some investors may face losses, the long-term societal value of this technology will be immense.

For now, the construction continues. The dust rises, the transformers hum, and across the American heartland, the factories of a new technological age are taking shape.