Report warns that the AI bubble will burst within 6 to 12 months, advising investors to maintain a neutral position on stocks in the short term and a low allocation in the medium term, while paying attention to forward-looking indicators such as analyst expectations and GPU costs.
Written by: Dong Jing
Source: Wall Street Insights
From the 19th-century railroads to 21st-century artificial intelligence, every major technological innovation in history has sparked a capital expenditure boom, but this enthusiasm often ends in a bubble burst.
BCA Research released a special report in November this year titled "When Capex Booms Turn Into Busts: Lessons From History," reviewing four typical capital expenditure booms, revealing the core logic of the transition from prosperity to collapse, and issuing a warning about the current AI craze.
The report summarizes five common patterns: investors overlook the S-curve of technology adoption, revenue forecasts underestimate the extent of price declines, debt becomes the core reliance for financing, asset price peaks occur before investment declines, and the collapse of capital expenditure exacerbates economic recession. These patterns are already emerging in the current AI field—technology adoption rates are stagnating, token prices have plummeted over 99%, corporate debt has surged, and GPU leasing costs have decreased.
Based on historical comparative analysis, BCA Research concludes that the AI craze is following the historical path of bubbles and is expected to end in the next 6 to 12 months. The report advises investors to maintain a neutral allocation to stocks in the short term and a moderately low allocation in the medium term, closely tracking forward-looking indicators such as analyst expectation revisions, GPU leasing costs, and corporate free cash flow.
The report particularly points out that the current economic environment adds to concerns, as U.S. job vacancies have fallen to a five-year low. If the AI craze fades without a new bubble to offset the impact, the upcoming economic recession could be more severe than during the 2001 internet bubble burst.
Historical Reflection: The Collapse Trajectories of Four Capital Frenzies
BCA states that the essence of capital expenditure booms is a collective optimistic expectation of the commercialization prospects of new technologies, but history repeatedly proves that such optimism often deviates from the objective laws of technology implementation, ultimately leading to collapse due to supply-demand imbalances, debt accumulation, and inflated valuations.
The 19th-century railroad boom in the UK and the U.S. showcased the destructive power of overcapacity.
The report notes that the success of the Liverpool-Manchester railway in 1830 ignited an investment frenzy in the UK, with railway stock prices nearly doubling between 1843 and 1845.
By 1847, railway construction expenditure accounted for a record 7% of the UK's GDP. A tightening of liquidity ultimately triggered the financial crisis in October 1847, with the railway index plummeting 65% from its peak.
The report states that the U.S. railroad boom peaked during the panic of 1873, forcing the New York Stock Exchange to close for ten days, with corporate bond default losses reaching 36% of face value between 1873 and 1875.
After the U.S. railway mileage peaked at over 13,000 miles in 1887, overcapacity led to a collapse in transportation prices, with about 20% of U.S. railway mileage entering bankruptcy management by 1894.
The electrification boom of the 1920s exposed the fragility of pyramid-like capital structures.
The report points out that the proportion of households with electricity rose from 8% in 1907 to 68% in 1930, but this process was mainly concentrated in urban areas.
Wall Street was deeply involved in this boom, promoting utility company stocks and bonds as "safe assets that widows and orphans could invest in," and by 1929, holding companies controlled over 80% of U.S. electricity generation.
The report states that after the stock market crash in 1929, the largest utility group, Insull, went bankrupt in 1932, reportedly wiping out the life savings of 600,000 small investors. U.S. electric utility construction expenditure peaked at about $919 million in 1930, then plummeted to $129 million in 1933.
The internet boom of the late 1990s confirmed that innovation does not equate to profitability.
BCA states that from 1995 to 2004, the annualized growth rate of U.S. non-farm productivity reached 3.1%, far exceeding subsequent periods.
However, the proportion of technology-related capital expenditure to GDP surged from 2.9% in 1992 to 4.5% in 2000, with over-investment placing immense pressure on corporate balance sheets.
The report notes that free cash flow in the telecommunications industry peaked at the end of 1997 and then declined continuously, crashing significantly in 2000. The Nasdaq Composite Index rose sixfold between 1995 and 2000, then plummeted 78% over the next two and a half years.
Multiple oil booms perfectly illustrate the cyclical nature of supply-demand imbalances.
BCA states that after the discovery of vast oil reserves in East Texas in 1930, daily production exceeded 300,000 barrels within 12 months, but the Great Depression exacerbated the situation, causing oil prices to plummet to 10 cents per barrel.
In 1985, Saudi Arabia abandoned production limits, leading to oil prices dropping to $10 per barrel.
Between 2008 and 2015, the U.S. shale oil boom increased crude oil production from 5 million barrels per day to 9.4 million barrels per day, while OPEC's refusal to cut production in 2014 caused oil prices to fall from $115 per barrel in mid-2014 to $57 by the end of the year.
Five Common Patterns: The Inevitable Path from Prosperity to Collapse
Reviewing the rise and fall of four typical booms, BCA Research summarizes five common patterns that provide key metrics for assessing the current trajectory of the AI craze. Specifically:
The first pattern is that investors overlook the S-curve of technology adoption.
Technology adoption is never linear; it follows an S-curve of "early adopters—mass adoption—laggards." Stock prices typically rise in the first phase, peaking in the second phase when the growth rate of adoption turns negative.
The current AI field is exhibiting this characteristic: most companies express intentions to increase AI usage, but actual adoption rates are showing signs of stagnation, with some indicators even declining in recent months. This divergence between "willingness and action" is a typical signal of the late stage of technology adoption.
The second pattern is that revenue forecasts underestimate the extent of price declines.
In the early stages of new technology, scarcity grants pricing power, but as technology becomes widespread and competition intensifies, prices inevitably drop significantly. From 1998 to 2015, the annual growth rate of internet traffic reached 67%, but the price of information transmission fell sharply. The price of solar panels has continuously declined since their inception, dropping 95% since 2007.
The AI industry is repeating this pattern: since 2023, the introduction of faster chips and better algorithms has led to token prices dropping over 99%. Despite the emergence of new applications like video generation, users' willingness to pay for such applications remains unclear.
The third pattern is that debt becomes the core reliance for financing.
In the early stages of a boom, companies can usually meet capital expenditure needs through retained earnings, but as investment scales expand, debt gradually becomes the primary source of financing.
In October 2025, Meta announced a $27 billion data center financing agreement through an off-balance-sheet special purpose entity; Oracle, after securing a $38 billion loan, raised another $18 billion in the bond market, with total debt nearing $96 billion.
More concerning is the case of "new cloud vendors" like CoreWeave, whose credit default swap rates rose from 359 basis points at the beginning of the month to 532 basis points by the end of October 2025.
The fourth pattern is that asset price peaks occur before investment declines.
Historically, in capital expenditure booms, asset prices such as stocks often peak before actual investment expenditures begin to decline. Even if investment expenditures fall from their highs, their absolute values may remain elevated, continuing to exacerbate overcapacity. This means that if investors wait for a clear signal of "investment decline" before taking action, they often miss the best opportunity.
The fifth pattern is that the collapse of capital expenditure exacerbates economic recession.
The bursting of a technology bubble typically occurs in two stages:
The first stage is the retreat of technology speculation, revealing overcapacity; the second stage is the collapse of capital expenditure dragging down the overall economy, leading to deteriorating corporate profits and creating a vicious cycle.
The report notes that the 2001 U.S. recession was not triggered by a deterioration in economic fundamentals but stemmed from the collapse of capital expenditure following the internet bubble burst. The rise of the real estate bubble in 2002 temporarily alleviated the impact of the internet bubble burst, but it remains uncertain whether a new bubble will emerge to offset the impact of the AI craze's collapse.
Risk Signals of the AI Craze: A Turning Point Within 6 to 12 Months
Based on historical pattern analysis, BCA Research believes that the AI craze is following the historical path of bubbles and is expected to end within the next 6 to 12 months. This judgment is based on multiple risk signals that have emerged in the current AI field.
From the perspective of technology adoption, the actual pace of AI implementation has not kept up with the fervent expectations of capital, with stagnation in enterprise adoption rates and consumers' willingness to pay for AI applications not yet fully validated.
From the price trend perspective, the significant drop in token prices has shown deflationary pressure, while the commercial value of new applications like video generation remains in doubt.
From the debt risk perspective, the financing structure of AI-related companies is increasingly reliant on debt, with some companies' credit risks beginning to surface.
The report suggests focusing on four key forward-looking indicators:
First, revisions of analysts' expectations for future capital expenditures; if the continuously rising expectations begin to level off, it could be a warning sign;
Second, GPU leasing costs, which have started to decline since May 2025;
Third, the free cash flow situation of hyperscale companies, which, while still at absolute highs, has shown a deteriorating trend;
Fourth, the emergence of a "metaverse moment," where the stock price of an AI company drops after announcing a major project, which will be a clear sign of a shift in market sentiment.
For investors, BCA Research recommends adopting a "moderately defensive" strategy. In the short term, maintain a neutral allocation to stocks for the next three months, and in the medium term, a moderately low allocation to stocks over the next twelve months, with a need to further enhance defensiveness in the coming months.
Specifically, closely track the aforementioned four forward-looking indicators to avoid passive adjustments when investment expenditures clearly decline; at the same time, consider defensive sectors and high-quality bonds to hedge against potential significant volatility in AI-related assets.
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