In late summer 2026, the global AI sector experienced a significant correction, with core AI concept stocks falling by approximately 20% from their June highs. Increased volatility and cautious investor sentiment reignited heated debate about whether the AI bubble was about to burst. A Barron's analysis pointed out that capital expenditure bubbles in transformative technologies will eventually burst, but the timing often lags behind market expectations. Based on historical investment thresholds, the probability of a complete short-term collapse of the current AI boom is low. This historical perspective can alleviate short-term panic, but it cannot mask the structural risks in the current market. This article argues that AI technology possesses long-term revolutionary value, but current capital market prices have excessively overdrawn future growth expectations. The market is at a fragile critical point, characterized by extreme overvaluation, extremely low risk compensation, and large-scale upfront investment, making medium-term valuation repricing a clear risk. Barron's Historical Perspective: The "Rule 25" of Capital Expenditure Bubbles A Barron's article, based on a review of 250 years of US economic history, summarizes the capital cycle patterns of transformative technologies such as railroads, electrification, and the internet: technology capital bubbles mostly end in overcapacity and valuation clearing, but the duration of the bubble usually far exceeds the pace of short-term market panic. The article extracts the key "Rule 25" reference standard: the upper limit of disruptive technology capital expenditure that an economy can be healthily absorb is approximately 25% of current GDP. Exceeding this threshold will lead to a concentrated outbreak of overcapacity, lower-than-expected returns, and pressure on the cash flow, triggering a systemic bubble burst. Based on the estimated US nominal GDP of approximately $30 trillion in 2026, the critical threshold for capital investment in the AI industry is around $7.5 trillion. Since the large-scale deployment of AI in 2024, investment across the entire industry chain has continued to expand: approximately $500 billion in AI chips, $350 billion in power infrastructure, $200 billion in data center hardware construction, and $100 billion in computing network infrastructure. Global tech giants predict that cumulative AI spending will reach $3.7 trillion by 2029. Adding the investments of various innovative companies, at the current growth rate, total industry investment is not expected to reach the risk threshold until the early 2030s. Based on this, the current AI capital cycle has not yet entered its final oversupply phase, and the probability of a short-term bubble burst is low. A complete technology capital cycle requires a closed-loop process of investment cooling, profit recovery, and self-sustaining growth. However, this pattern has significant limitations: "Rule 25" is an empirical threshold summarized after the fact, not a precise predictive tool. A bubble burst does not necessarily require investment to peak; tightening financing, slower-than-expected growth, changes in the competitive landscape, or shifts in risk appetite can all trigger local or even overall valuation adjustments in advance. Shiller Valuation Framework: US AI Stock Valuations Enter Historical Extreme Range Compared to long-term investment thresholds, the most pressing risk at present is historically extreme valuations. The Shiller Cyclical Adjusted Price-to-Earnings Ratio (CAPE) eliminates short-term earnings and inflation interference, serving as a core indicator for predicting long-term returns and drawdown risks. Higher CAPE valuations indicate lower long-term returns and a greater probability of deep drawdowns. As of August 2026, the S&P 500 CAPE valuation is in the 39-42 range, ranking in the 98-99th percentile since 1881, only lower than the peak of the dot-com bubble in 1999-2000, and far exceeding the 16-17 epoch average. Coupled with the historically high US stock market capitalization/GDP Buffett ratio, this sufficiently demonstrates that the current market has already overdrawn the growth and profit expectations of AI for the next decade or more, fully incorporating the optimal development scenario into prices, even resulting in excessive premiums. While the Shiller framework cannot precisely time the market, it fundamentally reshapes the market's risk-return structure. Starting at an extremely high valuation level, even minor negative variables such as lower-than-expected AI returns, macroeconomic fluctuations, and rising interest rates will be amplified by the market, triggering rapid and deep valuation corrections. It's important to clarify that this round of market activity is not a hollow bubble like the unprofitable, purely conceptual one of 1999. Leading AI companies possess stable profits and cash flow; the core risk is a valuation bubble, with market prices completely overdrawing optimistic expectations and leaving no room for error. Market anomalies: Compressed equity risk premium and sharp sector volatility The implicit core of the current market vulnerability is the extreme compression of the equity risk premium (ERP). The equity risk premium represents the excess return compensation investors receive for bearing stock market risk. When the stock earnings yield continues to decline relative to the risk-free real interest rate, it means that the cost-effectiveness of risky assets has significantly decreased, and market risk pricing is severely inadequate. Latest data from 2026 shows that the US stock equity risk premium has fallen to a near 30-year low, with some estimates hovering just over 1%. Extremely low premiums can only be maintained by ample liquidity and extreme optimism. Once financing costs rise, the growth rate of AI implementation slows, and liquidity tightens marginally, the premium will quickly recover, leading to a systemic decline in valuations. Meanwhile, the extreme volatility of the AI sector, with its continuous surges and plunges, is a typical warning sign of a high-valuation bubble. Leveraged ETFs, options hedging, quantitative momentum trading, and retail leveraged funds create a positive feedback loop, amplifying market volatility. This volatility is not market noise, but rather the market beginning to reprice the uncertainty of AI industry cash flow, a clear signal that the bubble is entering a fragile phase. Overseas Mirror: Lessons from the South Korean AI Market Correction The AI rally and deep correction in the South Korean stock market in 2026 provide a vivid risk example for global markets. Leveraging its advantages in memory chips and computing hardware, the South Korean KOSPI index surged within a year, with Samsung and SK Hynix's market capitalization approaching trillions, fueling extreme market optimism. However, the market reversed rapidly in June and July 2026, with the index retreating by over 40% in the short term, triggering circuit breakers multiple times, and wiping out $2-2.5 trillion in market capitalization within two months. This correction is not due to a failure of AI technology logic, but rather the result of a confluence of multiple risks: the market is questioning the long-term returns of AI infrastructure, and corporate performance is failing to meet overly high expectations; the rise of China's hardware industry is breaking monopolies and intensifying industry competition; and concentrated liquidation of highly leveraged retail investors in the local market further amplified the decline. The core lesson from the South Korean case is that in a market structure characterized by high valuations, high leverage, high concentration, and high expectation gaps, the speed and magnitude of risk release can far exceed changes in fundamentals. Secondary markets and niche sectors often expose risks first. While the main US stock market is more resilient, it is not immune, and when sentiment and capital inflection points, it will also face rapid valuation clearing. Multiple institutional evidence supports this: systemic risks of overheated AI investment . Analysis by authoritative global institutions further confirms the bubble nature of this round of AI capital frenzy. The Bank for International Settlements (BIS) 2026 annual report points out that the current AI investment boom is highly similar to historical bubbles in canals, railways, and the internet: genuine technological breakthroughs have attracted massive amounts of capital far exceeding the capacity for commercial returns, resulting in structural capital over-allocation. In 2025-2026, leading global tech companies' annual AI spending exceeded $1 trillion, surpassing their own free cash flow capacity. The industry generally relies on debt and non-standard financing to sustain expansion. If subsequent investment returns fall short of expectations, the financing environment will contract rapidly, turning the capital boom into a long-term investment recession. Coupled with hardware bottlenecks in electricity, computing power, and storage, market fragility will further intensify. GMO and Grantham have even listed the current US stock market as one of the biggest bubbles in US history, with the AI narrative being the core driver of high valuations. Institutions warn that a return of AI assets to historical valuation levels will trigger a significant correction, and AI investment has become a core pillar of US GDP growth; a cooling of the sector will significantly drag down overall economic growth. Furthermore, the market's "rolling bubble" theory suggests that this round of AI bubble will exhibit a cyclical contraction characteristic, with large models, computing power, and applications rising and falling sequentially, rather than a one-time, comprehensive collapse. Global rating agencies have also listed AI valuation corrections as a core annual capital market risk. Comprehensive Analysis and Investment Implications Based on historical patterns, valuation data, market volatility, and institutional perspectives, the core conclusion of the current market is clear: the AI bubble is unlikely to burst completely in the short term, but the risk of structural repricing in the medium term is extremely high. Four key characteristics—extreme CAPE valuations, low equity risk premiums, debt-driven upfront investment, and high volatility in both domestic and international markets—constitute the fragile foundation of the current AI market. It is crucial to strictly distinguish between two core logics: the long-term industrial value of AI technology is real and credible, but the technology's implementation involves significant time lags in returns. The risks of overcapacity, lower-than-expected returns, and leverage pressure resulting from large-scale upfront investment will continue to unfold over the next few years. Barron's "Rule 25" only proves that there is no systemic risk of a short-term collapse and cannot provide a safety net. Bubble triggers are flexible and changeable, and risks could structurally erupt at any time. Investors need to move beyond the binary opposition of "whether the bubble will burst" and clearly distinguish between "long-term positive technological outlook" and "valuations that have fully priced in expectations." Technological bubbles often last longer than expected, but valuations returning to fundamentals is an inevitable long-term trend. Compared to speculating on short-term turning points, tracking the real ROI of AI, the tightness of financing, market leverage, and signals from overseas secondary markets is more valuable in practice. In the current environment of high valuations and low risk compensation, the market risk-reward ratio has been severely imbalanced, and the market as a whole has entered a fragile operating phase of high risk and low return.