The Weekly Planet #8: Hyperscalers

In 2025, a handful of technology companies are engaged in what may be the most ambitious infrastructure buildout in human history. Amazon, Microsoft, Google, Meta, Oracle, IBM, and xAI - collectively known as "hyperscalers" - are investing approximately $400 billion this year in data center infrastructure to power artificial intelligence. To put this in perspective, this single year of spending exceeds the inflation-adjusted cost of the entire Apollo program that put humans on the moon. The question hanging over this unprecedented capital deployment is whether it represents visionary investment in a transformative technology or the inflation of history's most expensive bubble.

What Are Hyperscalers?

Hyperscaling refers to computing architecture's ability to scale seamlessly as demand increases. Hyperscalers are massive data centers that provide vast computing resources through elastic cloud platforms, enabling organizations to deploy and manage large-scale applications efficiently. Unlike traditional on-premises data centers, hyperscalers operate at immense scale, offering unparalleled performance, reliability, and flexibility.

The top hyperscalers - AWS, Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle - serve both external customers and run their own applications. Companies leverage hyperscalers to avoid managing physical infrastructure complexities like space, power, and hardware maintenance, while benefiting from dynamic resource scaling during peak demands. Their global footprint allows multinational organizations to deploy applications closer to users, and they're particularly suited for big data and analytics workloads requiring massive but intermittent capacity.

The term "hyperscaler" needs to evolve beyond its technical definition. It should describes companies not just for their computing architecture but for the planetary scale of their ambitions - drawing resources from across the globe, from rare earth minerals for chips to enormous energy supplies, and distributing their AI models worldwide. The concept suggests more than size; it implies an exceptionally rapid response to problems deemed critically important.

The Technical Infrastructure: Building at Breakneck Speed

The AI revolution is driving unprecedented technical demands. At the core is rapid GPU advancement—a computation task that once took 32 hours can now be accomplished in one second with the latest technology. This exponential improvement allows AI programs to train on increasingly larger datasets, accelerating the pace of innovation with each new GPU generation.

The scale is staggering. Globally, data center construction stands at record levels, with an estimated 10 gigawatts of capacity projected to break ground in 2025, and 7 gigawatts reaching completion. This translates to roughly $170 billion in asset value requiring either development or permanent financing this year alone. McKinsey projects that companies worldwide will need to invest between $3.7 trillion and $7.9 trillion in new data center capacity between 2025 and 2030, depending on how quickly AI adoption accelerates.

The infrastructure challenges are immense. Power infrastructure represents the primary bottleneck - in many markets, extending high-capacity power lines to new development sites takes four years or more, primarily due to securing easements and regulatory approvals. This has fundamentally shifted site selection criteria; land is now evaluated based on available power capacity and proximity to transmission lines rather than price or acreage.

Nuclear power, particularly small modular reactors (SMRs), is emerging as a preferred solution. SMRs can provide 1.5 to 300 megawatts of power and are modular and scalable, making them potentially ideal for data centers at a fraction of traditional large-scale nuclear costs. However, commercial deployment in the U.S. isn't expected until 2030 at the earliest. Multiple nuclear power purchase agreements were signed in 2024 involving both active and decommissioned plants slated for reactivation around 2028.

Thermal management represents another critical challenge. NVIDIA's latest AI chips consume up to 300% more power than their predecessors, and forecasts suggest global data center energy demand will double in the next five years. Liquid cooling has quickly become the default for new construction, with existing facilities retrofitting to accommodate higher-density workloads. Industry typically employs a hybrid approach of 70% liquid cooling and 30% air cooling. Immersion cooling, though promising for the most power-dense environments where GPUs push above 150 kilowatts per rack, faces challenges related to liquid quality, reliability, maintenance, and structural design—the largest cooling baths can reach four metric tons when filled, requiring significantly reinforced flooring.

Speed as Strategy: The xAI Case Study

No company better exemplifies hyperscaling's combination of scale and speed than xAI, whose Colossus 1 became the largest AI training cluster—roughly 200,000 H100/H200 GPUs plus 30,000 GB200 NVL72 chips—erected from scratch in just 122 days. At approximately 300 megawatts, it remains the largest fully operational, single-coherent cluster globally.

But Colossus 1 was merely prologue. xAI's Colossus 2 project, kicked off in March 2025, represents an even more audacious achievement. By August 2025, the company had built 200 megawatts of cooling capacity in six months—a task that took competitors like Oracle, Crusoe, and OpenAI 15 months. This is enough to power roughly 110,000 GB200 NVL72 chips.

xAI's genius move involved developing a gigawatt-scale energy hub across the border in Mississippi to avoid Tennessee's regulatory pushback. The company acquired a former Duke Energy power plant in Southaven and secured temporary approval from Mississippi regulators to run gas turbines for up to 12 months without a permit. Through partnership with Solaris Energy Infrastructure, xAI now controls approximately 1,140 megawatts in committed capacity, with plans to expand to over 1.5 gigawatts.

With SpaceX and Tesla as natural customers and allies, xAI demonstrates what's possible when a company combines compute infrastructure with captive energy production. However, this rapid expansion comes with a price tag in the tens of billions of dollars, requiring capital from sources including Saudi Arabia's Public Investment Fund and UAE state funds.

Financial Engineering: The Meta Model and SPV Strategy

Hyperscaling isn't just a technical challenge - it's a financial engineering marvel that increasingly resembles the creative accounting of past bubbles. Meta's approach exemplifies the trend: the company is raising $29 billion through a mix of $26 billion in debt and $3 billion in equity to rapidly build AI data centers, using special purpose vehicles (SPVs) to keep this debt off its balance sheet.

This financial structure allows Meta to avoid showing large liabilities directly while retaining control over assets. Private equity firms and insurers, seeking higher yields without excessive risk, fund these SPVs. They can charge 200-300 basis points above investment-grade rates - a juicy premium - while feeling confident they'll be repaid by companies like Meta.

For Meta and similar companies, this makes sense because the capital needed for buildouts is so enormous that using orthodox balance sheet debt would significantly damage their financial statements. By structuring investments through SPVs where they have joint ownership, companies don't have to show the debt as their own. It's accounting trickery, transparently attempting to raise money without balance sheet damage by putting debt in indirectly controlled vehicles that, for accounting purposes, don't appear as their debt. The technical term is "control without consolidation."

This arrangement creates a powerful incentive loop between structurally overcapitalized insurers, return-hungry private equity firms, and mega-cap companies trying to avoid looking leveraged. Everyone benefits until something breaks.

The risks are substantial. The illusion of off-balance-sheet financing makes risk murky. Yes, the SPV isn't technically on Meta's balance sheet, but for practical purposes, it exists to serve Meta, is controlled by Meta, and its default would materially impact Meta. The massive capital influx encourages overbuilding of highly capital-intensive infrastructure with speculative returns, cushioned by only thin equity buffers. Additionally, insurers backing these SPVs face asset-liability mismatches, as their long-term liabilities may not align with illiquid, concentrated investments in AI data centers.

Economic Distortions and the Capital Black Hole

The hyperscaler buildout is creating unprecedented distortions in the broader economy. In the first half of 2025, AI capital expenditure contributed more to U.S. GDP growth than consumer spending—the first time this has occurred in modern economic history. Paul Kedrosky estimates that AI capex is nearing 2% of total U.S. GDP by itself, "affecting economic statistics, boosting the economy, and beginning to approach the railroad boom."

This concentration of investment creates a capital black hole, diverting resources from other sectors. The pattern echoes the 1990s telecom boom when massive capital spending in one narrow slice of the economy starved small manufacturers of capital, increasing their cost of capital and forcing higher margins. Some people claim that this might have increased off-shoring to China by American manufacturers who couldn’t find investors in the US. Today, private equity firms prefer large investments in data centers over smaller manufacturing ventures, exacerbating this shift. Data centers are attractive - who wouldn't prefer to lend to one Mark Zuckerberg instead of 10,000 small businesses?

The geographic concentration is equally striking. Enormous sums flow to very narrow recipients in small geographies like Northern Virginia. A $15 billion investment in a single AI data center in Ellendale, North Dakota - equal to a quarter of the state's annual economic output - illustrates the scale. Yet most money goes into chips, energy, and cooling with very few jobs created. Data center economics "disembed" installations from their surroundings, spending massive amounts without proportional local economic impact.

The energy demands contribute to another distortion: energy inflation. Data centers' voracious power consumption is driving up electricity costs, potentially causing consumer backlash and prompting some construction to move offshore.

The Bubble Question

The AI bubble operates on three distinct levels:

The Metaphysical-Technical Bubble: Will AI achieve or exceed human-level performance across all domains? When discussing AGI (Artificial General Intelligence) or ASI (Artificial Superintelligence), this is the bubble in question—the fundamental question of AI's ultimate capabilities.

The Adoption Bubble: Even if AI approaches AGI levels, will companies adopt it? Will it change productivity? Will it help create new, better products? Usage patterns suggest caution—some reports indicate AI usage is actually declining at large companies still trying to figure out how large language models can save money.

The Investment Bubble: Even if AGI is at hand and transforms production, will the vast sums poured into AI infrastructure ever produce profits? This is where current bubble accusations concentrate.

Here, we are talking only about the investment bubble.

The mathematics are sobering. Leading AI firms generate approximately $50 billion in annual revenue. Yet $2.9 trillion in cumulative investment is forecasted between 2025 and 2028, excluding energy costs. American consumers spend only about $12 billion annually on AI services—roughly Somalia's GDP. Analysts estimate that $2 trillion in annual AI revenue by 2030 is needed to justify current investments, exceeding the combined revenue of major tech firms.

CoreWeave, a key player leasing data centers and chips, has amassed over $42 billion in contracts and $15 billion in debt. Companies pour hundreds of billions into AI infrastructure yet report little to no return on investment. One startup, Thinking Machines, raised a $2 billion seed round at a $10 billion valuation without releasing a product or telling investors what they're building.

The economics of GPU depreciation compound the problem. Unlike railroads or telegraph infrastructure that lasted generations, AI hardware like GPUs becomes obsolete within two to three years. GPUs represent over half of data center expenses, and this rapid obsolescence creates a challenging environment for companies to recoup investments. Private equity analyst Harris Kupperman estimates that American data centers would need to generate $480 billion in 2025 alone to make profits comparable to similar-sized ventures—a tenfold increase from current returns.

When Will the Bubble Burst?

If the investment bubble exists, analysts suggest it could burst within 2-2.5 years as operating costs and declining rental prices make data centers economically unsustainable. The pattern echoes historical infrastructure booms: railroads, telegraphs, and broadband all experienced build-crash-transform cycles. AI will likely follow the same arc - rising first, crashing second, and eventually changing the world.

Several factors could precipitate a crisis. The thin equity cushion in many SPV structures means a 10% buffer is wildly insufficient if projected AI workloads stall or margins compress. If returns falter, the fallout could expose vulnerabilities in the intersection of private credit, insurance capital, and tech infrastructure financing. While not yet systemic risk on the scale of the 2008 financial crisis - partly because no one has yet packaged and syndicated cash flows from SPV controlled data centers (there are no subprime GPUs yet) the components are familiar: leverage hidden in plain sight, mispriced risk, and capital chasing yield through increasingly convoluted structures.

However, defenders argue that even if AI spending proves excessive, the infrastructure retains value. A company that built a giant data center for AI still has a giant data center. These are durable assets—hardware and buildings—that can bootstrap whatever technology wave comes next. This distinguishes the current boom from purely speculative bubbles.

Conclusion: Planetary Stakes

Hyperscaling is a planetary-scale reorganization of resources. The concept captures both the geographic reach of these operations and their potential impact on humanity's technological future. Whether the current trajectory leads to transformative breakthroughs or spectacular collapse, the hyperscaler buildout is already reshaping the global economy.

The greatest uncertainty lies not in AI's technical capabilities or even its eventual adoption, but in timing. Will returns materialize fast enough to justify the unprecedented capital deployment? Can the financial engineering supporting the buildout withstand a period of disappointing results?

History suggests that even transformative technologies can't escape economic gravity. The internet did change the world, but not as quickly as early champions promised, and many who got ahead of themselves were humbled in the process. Yet the infrastructure built during that earlier boom—the fiber optic cables laid in the late 1990s—ultimately enabled the digital economy we inhabit today.

The hyperscalers are making a bet that AI will follow a similar pattern, with the added twist that they're spending in months what previous booms spent over years. Whether this represents visionary investment or catastrophic overreach, the answer will reshape not just the technology industry but the global economic order, perhaps even planetary resources and humanity's technological trajectory.