AI's Fragile Safety Line—One Step Away From Disaster
The AI boom of 2025 is not just another tech craze—it’s an industrial and financial bubble unfolding in real time. Like the 1896 British bicycle mania, genuine breakthroughs have collided with speculative excess, turning innovation into promotion.
Recent discussions in financial media and among investors show growing unease about AI infrastructure spending and whether we’re inflating one of the largest bubbles in history.
Even insiders admit the tension. Sam Altman, hardly a skeptic, concedes that speculative dynamics are forming. Yet he and his peers are trapped in a prisoner’s dilemma: under-investing feels riskier than over-investing, and faith in long-term productivity gains justifies almost any burn rate.
Still, “bubble” and “benefit” aren’t mutually exclusive. Like the railways, canals, electric grids, or dot-com companies of past centuries, the AI boom is both an overreach and a technological foundation. Those earlier manias ended in bankruptcies but left behind assets that powered decades of growth. The better question, then, isn’t whether this is a bubble—but how the system behaves under stress.
What the AI Capital Cycle Does and Does Not Resemble
Most famous bubbles are the wrong map. The dot-com boom was about unproven websites and ad models; 2008 was about household leverage and bad collateral; Dutch tulip mania of 1636 left nothing useful behind.
A closer parallel lies in the late-1990s TMT and fiber build-out. Telecom firms installed networks years ahead of demand, many went under, yet the physical backbone became indispensable. That industrial pattern—massive capital outlays, delayed returns, eventual utility—mirrors today’s AI spree in GPUs, data centers, and power.
But to understand the interweaving technological innovation and financial psychology of the moment, the sharper analogy comes from an older episode: the Great British Bicycle Bubble of 1896.
The Great British Bicycle Bubble
In the 1890s, a wave of inventions—pneumatic tires, chain drives, lighter steel, the diamond-frame design—made the bicycle fast, comfortable, and suddenly attainable. For the first time, ordinary people could move freely without trains or horses. Demand soared. Finance followed.
Between 1895 and 1897, about six hundred bicycle firms went public in London, and share prices tripled before gravity returned. Promoter Ernest Terah Hooley embodied the mania. He bought Pneumatic Tyre for £3 million (mostly funded with bank loans), rebranded it as Dunlop, hyped it furiously, and flipped it for £5 million—a Victorian leveraged buyout masterstroke that sparked a gold rush of imitators.*
As production boomed, margins collapsed. Cheaper American bikes flooded Britain at half the price. By 1901, more than seventy per cent of local makers had disappeared. Yet the bicycle endured, democratizing mobility and paving the way for motorcycles and cars. Technology triumphed. Capital did not.
AI is walking a similar path. Transformer breakthroughs made automated reasoning cheap and scalable. If bicycles freed people from horses, AI frees them from boring work. But progress has again fused with speculation. Financialization now amplifies every genuine gain. The winner-take-all narrative—“invest or be left behind”—feeds reckless spending that assumes growth without end. History suggests otherwise: tech winners can emerge after the crash as well—Uber, Tesla, and Meta are the most prominent examples.
*Note: Hooley was later exposed as a serial fraudster—repeatedly bankrupted and convicted for false pretenses in other ventures.
The Mt. Hood Disaster
In 2002, a roped team on Oregon’s Mount Hood suffered a fatal chain fall. On hard, early-morning ice and a steep slope, the lead climber slipped. With no intermediate anchors (pickets, ice screws) to absorb the force, the rope transmitted the fall downhill, yanking each teammate off in sequence and dragging multiple parties into a pileup. Rope travel works on moderate terrain because partners can arrest a minor slip with their axes; that everyday success creates a false sense of security. But when the leader falls on hard, steep ground—where momentum builds instantly—the same rope becomes a coupling device that multiplies risk, turning one error into a system-wide failure. As one experienced Hood climber put it, “a rope without fixed protection is a suicide pact.”
Today’s AI ecosystem resembles that rope. Model developers such as OpenAI and Anthropic set the narrative—“if we build it, they will come.” Hyperscalers like Microsoft, Google, and AWS translate that belief into massive CapEx and long-term contracts. Suppliers, lenders, and startups are tied in through equity stakes, take-or-pay agreements, and debt secured by projected demand. The system can absorb small slips—a missed quarter or delayed launch—but a major stumble at the top can cascade through the chain.
As Bloomberg’s network maps show, the interconnections run deep: Microsoft and Nvidia hold stakes in OpenAI; Amazon owns part of Anthropic; OpenAI holds warrants in AMD; smaller firms like CoreWeave borrow against rapidly depreciating GPUs. What looks like diversification tightens the knot—everyone’s safety line is tied to the same deadly rope.
History in Real Time
The AI surge carries both signatures—it is an industrial and a financial bubble at once. Like the bicycle mania, the technology is transformative and will outlast the bust. But the dense web of cross-holdings, long-dated contracts, and debt-financed build-outs has turned what could have been a healthy investment cycle into a tightly coupled system. Stress at one node now travels instantly to another.
The smaller, leveraged players—GPU lessors, data-center developers, PE-backed power suppliers—are climbing without anchors. When liquidity tightens or utilization disappoints, they’ll tumble first. The giants—Microsoft, Nvidia, Amazon—are more likely to survive and consolidate the wreckage, but not before a costly shake-out.
For investors, the lesson is plain. Bubbles built on leverage and interdependence last longer than reason allows, yet when they break, they fall fast. AI’s promise is real; the coming destruction, equally so. The wise move is to admire the technology, respect the cycle, and stand clear while the music is still on.
Source: Bloomberg, Nasdaq, Deep Survival by Laurence Gonzales
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