Don’t be Fooled—the AI Bubble is the Strategy

From railroads to dot-coms to AI, speculative cycles remain central to how capital accumulates and offloads risk

Fiber optic cable-laying train at Isle Royale Sands, Houghton. May 19, 1987. As the internet backbone became increasingly privatized, rapid fiber expansion created a fiber glut that contributed to the economic fallout following the dot-com bust in the early 2000s.

Mainstream industrial policy analysts often mistake infrastructure booms for evidence of economic renewal. In doing so, they overlook the deeper speculative dynamics that underpin these cycles of expansion. Recent coverage in the Wall Street Journal describes the AI infrastructure surge as a new “age of infrastructure,” lauding the record $102.5 billion in capital expenditures by Google, Meta, Microsoft, and Amazon as a sign of industrial strength. Going further, Axios characterizes AI-related investments as an “AI super-stimulant” propping up the U.S. economy amid broader weakness. Bank of America has projected $700 billion in AI capex through 2026, framing this as a bulwark against stagflation (although the reality may be a little different). These narratives treat investment volume as inherently productive, even as analysts are beginning to warn of overbuild risk, speculative finance, and fragile debt structures.

We’d do well to look past the glossy narratives and see AI “innovation” economy for what it really is—capitalism with faster processors.

Along with the acceleration, the underlying dynamics of fast capitalism haven’t changed all that much—speculative hype inflates asset values, insiders extract returns early, and risks are ultimately offloaded onto the public or “dumb money.” We can see that the financial press has begun to reflect this growing concern about an AI bubble:

• Fortune (July 22, 2025): “The AI Boom Is Now Bigger than the ’90s Dotcom Bubble.”

• Financial Times (July 30, 2025): “What’ll Happen If We Spend Nearly $3tn on Data Centres No One Needs?”

• Wall Street Journal (August 3, 2025): “The AI Boom’s Hidden Risk to the Economy.”

Like the railroad and dot-com booms before it, the AI bubble is not a deviation from normal economic cycles—it is a structural feature of capitalism itself. Far from being an accidental byproduct, it is deliberately constructed—extraction is repackaged as innovation, hype replaces substance, and inflated promises attract speculative capital. This process is actively supported by an industrial policy and regulatory apparatus oriented toward private gain. The bubble is not a flaw in the system—it is how the system works.

The AI sector has emerged as the primary engine of economic optimism in an otherwise fragile U.S. economy.

Driven by enormous capital expenditures in data centers, chips, and energy infrastructure, firms like Microsoft and Meta are now allocating over a third of their total revenue to AI infrastructure alone. These investments have contributed more to GDP growth over the past two quarters than all consumer spending combined.

This rapid expansion is not powered by organic revenue, but by a speculative financial engine. Venture-backed firms like CoreWeave—originally a crypto miner—have reached valuations as high as $19 billion with minimal revenue, raising $650 million in secondary sales and targeting a $1.5 billion IPO. Microsoft’s finance leases, mostly tied to infrastructure, have tripled since 2023 to $46 billion, while Meta is seeking $30 billion in private credit for its data center expansion. These are not isolated cases—they point to a broader shift in the structure of financing behind AI growth.

What we are witnessing is not a conventional investment cycle but a high-leverage system built on opaque instruments and limited oversight. Early dismissals of systemic risk of the AI bubble—such as those made in 2019, when its expansion was still thought to be largely equity-financed—have become outdated. Since the release of ChatGPT in 2022, the sector’s capital demands have surged, outpacing equity investment and pushing firms more heavily into debt markets.

Morgan Stanley projects that AI infrastructure spending could reach $2.9 trillion by 2028, leaving a $1.5 trillion financing gap. To close it, firms are turning to complex forms of credit: multi-billion-dollar loans backed by GPUs, drawing on venture debt, and leveraging structured credit based on projected revenues. The private credit market for data infrastructure—virtually nonexistent in 2018—now exceeds $50 billion and is largely funded by insurance companies, private equity firms, and traditional banks. According to the Boston Fed, U.S. banks now lend 14% of their commercial portfolios to non-bank financial institutions like private credit funds.

If AI revenue projections fail to materialize, the fallout will extend far beyond Silicon Valley. A downturn would ripple through the broader financial system, revealing just how much institutional risk has been taken on to underwrite the illusion of limitless AI-driven growth.

Historical precedents abound.

During the late 19th and early 20th centuries, the rise of the railroads gave way to a series of speculative bubbles built on lofty promises and shaky financial foundations. The Crédit Mobilier scandal of the 1870s saw Union Pacific executives siphon off $44 million in government contracts through a fake construction company, exposing the extent to which public infrastructure projects could be used for private gain. The ensuing panics of 1873 and 1893 were triggered in part by the overbuilding of railroads fueled by debt, subsidies, and inflated valuations. In the 1920s, the Van Sweringen brothers built a 28,000-mile rail empire through a pyramid of holding companies financed largely through debt and public securities, despite having little direct equity. Revenues declined with the rise of the automobile, but asset values remained artificially high until the 1929 crash revealed the empire’s hollow core—leading to widespread bankruptcies and community collapse during the Great Depression.

Just as the internet was built (literally) within the rail system’s rights-of-way, the dot-com bubble of the late 1990s followed a similar speculative path. Startups with no viable revenue models attracted massive investment on the promise of revolutionary innovation, often underwritten by investment banks eager to feed the hype. IPOs were deliberately underpriced to generate dramatic first-day surges—VA Linux soared 698% on debut—while analysts issued glowing ratings to maintain deal flow. Profitability was replaced by speculative metrics like “eyeballs,” and when lock-up periods expired, insiders dumped their shares. As in the railroad era, the crash was swift and brutal: between 2000 and 2002, the NASDAQ lost 78% of its value, erasing $5 trillion in market capitalization and leaving pension funds, retail investors, and employees to absorb the damage.

Both eras were marked by exuberant narratives of transformative infrastructure, underwritten by financial institutions that prioritized hype over fundamentals. In each case, insiders and early investors cashed out before the bust, leaving everyone else to absorb the fallout. The physical networks—rail lines and fiber optic cables—persisted, but the financial structures collapsed under their own weight.

The current AI cycle is more complex but no less extractive.

AI infrastructure spending now exceeds telecom investment during the dot‑com bubble. U.S. tech giants are on track to spend between $320 and $350 billion on AI-related infrastructure in 2025 alone. These investments are also reshaping land, energy, and water systems in major ways that tether these resources and their governance to speculative buildouts across many states. Northern Virginia, Georgia, and parts of Texas have become epicenters of AI infrastructure. State and municipal governments are offering enormous tax breaks—often exceeding $2 million per permanent job created. Permitting is being streamlined through federal executive orders and state preemption laws. Many data centers are being colocated with legacy fossil fuel infrastructure or built on repurposed industrial land, including former steel mills and nuclear power sites.

The environmental and social costs are mounting. Data centers already consume over 4% of U.S. electricity, and that share is expected to triple by 2030. Their water demands reach into the millions of gallons per day in some regions, intensifying pressure on already strained systems. These facilities are increasingly powered by coal- and gas-intensive grids. The costs—energy procurement, transmission upgrades, water delivery—are being passed on to ratepayers through long-term utility contracts. Meanwhile, grid reliability is deteriorating, and local communities face infrastructure stress without commensurate public benefits.

Discontent is growing. In education, the American Association of University Professors (AAUP) has pushed back against AI edtech mandates, citing degraded student learning conditions and loss of academic freedom. Students describe AI-augmented education as alienating and ineffective. Artists and journalists have begun to organize against what they see as the expropriation of their work by generative AI tools. Public sentiment toward AI remains mixed at best—widely viewed as disruptive, extractive, and imposed without consent.

These tensions are structural, not incidental.

The AI boom, like previous speculative cycles, is propelled by inflated promises and strategic narratives of national competitiveness—but behind the scenes, it operates as a mechanism for transferring risk from capital to the public. The architecture of this risk has long been in place, papered over by the language of innovation and progress.

Historically, each phase of speculative infrastructure development has left behind overbuilt systems, concentrated wealth, and long-term costs borne by society at large. The railroads enabled telegraph and telecom networks from which the dot-com bubble laid the digital backbone for cloud computing. Today’s AI expansion follows the same pathways—quite literally, in the case of fiber laid along railroad rights-of-way—drawing on deregulated energy markets, subsidized land deals, and even Cold War-era industrial zones once left behind by corporate giants in search of new frontiers. It repurposes the material residue of past cycles while reproducing their core logic—private enrichment through public exposure.

Whether this current boom ends in a sharp collapse or a slow deflation remains uncertain. The more urgent question, then, is what follows the bust. Will there be public reckoning and structural reform, or a deeper consolidation of corporate power as crisis is leveraged to entrench control?

Further reading:

• Baran, Paul A., and Paul Marlor Sweezy. 1966. Monopoly Capital: An Essay on the American Economic and Social Order. NYU Press.

• Starr, Paul. 2002. “The Great Telecom Implosion.” The American Prospect, September 8, 2002. https://www.princeton.edu/~starr/articles/articles02/Starr-TelecomImplosion-9-02.htm.