Executive Summary
Markets move in cycles of innovation and speculation, and the present surge in artificial intelligence is no exception.
Today’s AI boom displays nearly all the features that have defined past bubbles—soaring valuations, concentrated flows of capital, euphoric investor sentiment, and media narratives that reinforce expectations of unstoppable growth.
By most measures, the parallels extend beyond resemblance: the speculative fervor around AI rivals and in many ways exceeds the South Sea Bubble, the 1840s railway mania, the 1920s boom, the dot-com era, and the subprime mortgage frenzy.
At the center of the storm lies a combustible mix of genuine technological promise, abundant liquidity, and human psychology. Investors see the potential for world-changing transformation, credit remains accessible enough to fuel risk-taking, and fear of missing out drives behavior to extremes.
The result is an environment where both startups and established firms are valued as though flawless execution, relentless hypergrowth, and immediate mass adoption were inevitable.
Such assumptions are unsustainable.
Every historical bubble has revealed the danger of expectations drifting too far from reality. In the late 1990s, the mantra was that profits no longer mattered; today, many AI firms are projecting revenues and margins based on unproven scenarios.
When the gap between projections and actual results grows wide, the risks compound. Financial losses are the most visible outcome, but history shows that misconduct often follows.
From the railway booms of the 19th century to Enron, WorldCom, and the more recent mortgage excesses, periods of extreme optimism have created fertile ground for creative accounting, misrepresentation, and fraud.
The warning signs are well known.
Extreme valuations relative to tangible earnings, heavy concentration of capital in a handful of celebrated winners, and the easy availability of venture funding or leverage all raise systemic risk. The proliferation of complex financial products magnifies fragility, turning small disruptions into cascading stress events.
And as always, the insistence that “this time is different” echoes loudly in the background, emboldening herd behavior while discouraging sober analysis.
When both retail and institutional investors chase momentum trades rather than fundamentals, the system edges closer to its breaking point.
History also offers a guide to navigating these environments.
Investors who wish to participate in innovation without being consumed by its excesses must apply a disciplined, historically informed framework.
This means challenging assumptions behind valuations, scrutinizing profit projections, monitoring leverage and liquidity conditions, and examining whether business models are robust enough to withstand shocks.
It also means cultivating behavioral awareness—recognizing that FOMO, herd instincts, and narrative intoxication can overwhelm even the most seasoned decision-makers.
Perhaps most critically, vigilance against aggressive accounting or unrealistic guidance is essential, since the incentives for embellishment grow strongest in speculative peaks.
This study is not written from a position of permanent pessimism. It is a general exploration of the anatomy of market crashes, designed to provide a framework for understanding why speculative cycles form and how they unwind.
At the same time, it is timely. The conditions we observe today suggest that a sharp correction is not a distant possibility but a near-term risk.
And our goal is not to preach fear.
Rather, it is to help prepare readers for the rogue waves that history tells us appear just when the waters seem calmest.
We believe extraordinary opportunities will emerge once excesses are flushed from the system, but to capture them, investors must first survive the volatility that lies ahead.
The sections that follow build upon this foundation, beginning with the AI boom itself.
As the most vivid present-day example of innovation colliding with speculation, it provides a live case study of how opportunity and risk entwine, setting the stage for both painful collapse and enduring renewal.
Background
Financial markets have always swung between fear & greed, moments of stability & episodes of mania.
Crashes are rarely a product of truly unforeseen shocks; more often, they are the inevitable outcome of long stretches of investor euphoria.
These euphoric phases are characterized by an intoxicating mix of extreme optimism, soaring valuations, the easy availability of credit, and the conviction that some new technological or economic paradigm justifies abandoning the lessons of history.
In hindsight, signals of excess usually appear glaring. But in the moment, investors, institutions, and even regulators are lulled by persuasive narratives and the apparent reliability of ever-rising prices.
This paper examines the anatomy of such euphoric cycles. It explores the conditions that allow optimism to grow unchecked, the signals that can be seen in real time, the distortions that only reveal themselves after collapse, and the enduring lessons investors can carry forward.
Historical examples ranging from tulip mania to the dot-com boom to the SPAC frenzy of 2021, paired with data on valuations, leverage, IPOs, and liquidity, provide the lens through which we analyze how manias build, why they unravel, and how disciplined investors can prepare for their aftermath.
Conditions That Breed Euphoria
Euphoria tends to be powered by three main elements:
1. Inflated valuations.
2. Abundant credit.
3. And a compelling narrative of progress.
Valuation is the most immediate signal. Robert Shiller’s CAPE ratio offers a century-long view of how earnings multiples expand during speculative eras.
In 1929, CAPE rose above 32 before collapsing to 5 in the depths of the Depression. In 2000, at the height of the dot-com bubble, it touched 44—a record that stood until today’s era, when CAPE once again surged into the high-30s.