Author: Zhang Feng

Artificial Intelligence (AI) is reshaping the global economy and financial landscape at an unprecedented pace. As the capital markets continue to show enthusiasm for AI-related companies, an inevitable question arises: Are we witnessing a speculative frenzy similar to the internet bubble of the late 1990s?

In 2025, Federal Reserve Vice Chairman Philip N. Jefferson systematically elaborated on his comparative analysis of the current AI boom and the internet bubble era at the Financial Stability Conference of the Cleveland Federal Reserve Bank. He proposed four key indicators to assess whether there is a bubble in AI. This speech not only reflects the cautious observation of emerging technologies by the world's most important central bank but also provides market participants with a clear framework for rationally evaluating the AI boom.

I. The Federal Reserve's Observational Basis: Dual Mandate and Financial Stability

All policies and observations of the Federal Reserve revolve around its statutory "dual mandate"—maximizing employment and ensuring price stability. Jefferson clearly pointed out that assessing the impact of artificial intelligence must start from this fundamental task. This means that the Federal Reserve's interest in AI is not only about its technological breakthroughs or market performance but also about how it affects overall employment levels, labor productivity, economic growth potential, and inflation trends.

From the employment perspective, AI exhibits a dual effect. On one hand, it promotes employment by enhancing work efficiency and creating new positions (such as AI research, deployment, and maintenance); on the other hand, its automation substitution effect may lead to the shrinkage of certain occupations, particularly impacting younger and less experienced workers. Jefferson noted that if AI merely replaces existing labor without simultaneously creating new jobs, it could trigger a short-term economic slowdown. This dynamic balance of "substitution and supplementation" is central to judging AI's structural impact on the labor market.

From the perspective of price stability, AI's increase in productivity helps lower production costs, exerting downward pressure on prices. Efficient resource allocation, supply chain optimization, and decision-making assistance are applications that may suppress inflation. However, at the same time, the construction of AI infrastructure (such as data centers) raises the prices of inputs like land and energy, and rising salaries for AI talent may also lead to cost-push inflation. This dual impact makes the net effect of AI on inflation highly uncertain and requires continuous monitoring.

To achieve the dual mandate, a robust and resilient financial system is crucial. The Federal Reserve continuously monitors systemic risks through its semi-annual Financial Stability Report (FSR). The latest survey shows that 30% of market contacts view "a shift in attitude towards AI" as a significant risk to the financial system, a substantial increase from 9% in the spring. This seems to warn that if market optimism about AI suddenly reverses, it could trigger tighter financial conditions and economic downturns. Therefore, the Federal Reserve's inclusion of AI in its financial stability monitoring framework is aimed at preventing asset bubbles and financial vulnerabilities that may arise from technological booms.

II. Monitoring Framework: FSR and Market Sentiment Tracking

The Federal Reserve's monitoring of AI is not conducted in isolation but is embedded within its overall financial stability assessment system. The FSR not only focuses on traditional risks such as leverage, asset valuation, and financing risks but also incorporates structural changes brought about by emerging technologies. Jefferson emphasized that policymakers must distinguish between "cyclical fluctuations" and "structural changes," and AI is likely to belong to the latter. This means that the productivity gains brought by AI may alter the relationship between employment and inflation, thereby affecting the transmission mechanism of monetary policy.

Market sentiment is one of the key focuses of the FSR. Surveys indicate that nearly one-third of market participants are aware of the potential risks of an AI sentiment reversal. This consensus itself may become a "self-fulfilling prophecy"—once the optimistic narrative shifts, rapid capital withdrawal could lead to severe asset price adjustments. Compared to the internet bubble era, today's speed of information dissemination and the prevalence of algorithmic trading may amplify market volatility. Therefore, the Federal Reserve's tracking of sentiment indicators is essentially an early warning of potential systemic risks.

Additionally, the application of AI within the financial industry itself presents new monitoring challenges. AI tools such as high-frequency trading, smart investment advisory, and risk models enhance efficiency but may also introduce new homogenization risks and pro-cyclicality. The Federal Reserve is strengthening its identification and assessment of these emerging risks by expanding its analytical toolkit (including the use of AI technologies themselves).

III. Four Core Indicators: The Touchstone for Judging AI Bubbles

Jefferson distilled four key differences by comparing the current AI boom with the internet bubble of the late 1990s, which can serve as core indicators for assessing whether there is a serious bubble in the current AI sector.

(1) Profitability Basis: From "Story-Driven" to "Profit-Supported"

During the internet bubble, many companies went public solely based on the ".com" concept, lacking sustainable profit models, with meager or even zero revenues, relying on external financing and market frenzy to maintain operations. In contrast, leading companies in the current AI sector (such as some tech giants) generally have solid and diverse profit channels. They not only generate revenue directly from AI services but also deeply integrate AI into existing product systems, enhancing the competitiveness of their core businesses. This "profit-supported" development model makes AI investments more fundamentally sound, reducing the space for pure speculative trading.

However, Jefferson also pointed out that the activity in the private equity market may partially obscure the profitability challenges faced by early AI companies. A large amount of venture capital is flowing into AI startups, which, although not publicly listed, have high valuations; if they fail to achieve profitability in the future, they could still become a source of risk. Therefore, the observation of profitability indicators must consider both public and private markets.

(2) Valuation Levels: Relatively Moderate Price-to-Earnings Ratios

At the peak of the internet bubble, internet companies often had price-to-earnings ratios reaching hundreds or even thousands, reflecting the market's irrational optimism about long-term growth. Currently, although the stock prices of AI concept companies have risen significantly, their price-to-earnings ratios remain far below historical peaks. This somewhat indicates that while investors are enthusiastic about AI, they are still anchoring to the actual profits and cash flows of companies to some extent.

Of course, the reasonableness of valuations needs to be judged comprehensively in conjunction with industry characteristics and growth stages. As a general-purpose technology, AI has enormous long-term value creation potential, and a moderate premium is reasonable. However, if valuations rise too quickly and detach from fundamentals, it could still give rise to bubbles. The Federal Reserve's focus on valuation indicators is to discern the rational components of market enthusiasm from overheating signals.

(3) Number of Public Companies: Limited Speculative Breadth

Between 1999 and 2000, over 1,000 internet companies went public, creating a "blooming everywhere" speculative frenzy, where even a name change to include ".com" could boost stock prices. Currently, there are about 50 publicly listed companies clearly classified as "core AI companies" (based on specific indicators), a number far fewer than during the internet bubble. This indicates that market speculation is relatively concentrated and has not yet spread throughout the entire market.

However, Jefferson also cautioned that the private equity market may hide a large number of AI startups that, while not publicly traded, are actively engaged in financing activities. If these companies go public in bulk in the future or if the financing environment changes dramatically, they could become new sources of instability. Therefore, the "number of companies" indicator needs to be dynamically observed, covering both public and private sectors.

(4) Financial Leverage: Lower Dependence on Debt

During the internet bubble, many companies relied on equity financing, with limited debt leverage, which somewhat reduced the direct impact of bubble bursts on the financial system. Currently, AI companies also rely less on debt financing, which helps limit risk transmission. However, recent trends show that to support massive investments in AI infrastructure (such as data centers and computing clusters), some companies are beginning to increase bond issuance and credit financing.

Jefferson specifically pointed out that as AI expands from software to hardware infrastructure, the demand for capital investment is rising sharply, which may lead to a gradual increase in leverage ratios. If AI sentiment reverses, highly leveraged companies will face greater debt repayment pressure, potentially spreading risks through credit channels to broader economic areas. Therefore, the leverage indicator needs close monitoring of its evolving trends.

IV. Implications for Market Practitioners

Jefferson's discourse not only provides an analytical framework for policymakers but also brings important insights for investors, companies, and researchers:

First, observations should start from the fundamental tasks of the observer. Investors should go beyond short-term market sentiment and deeply analyze the substantive impact of AI technology on corporate fundamentals (profitability, cost structure, competitive barriers). Companies should focus on how AI can enhance their productivity and long-term competitiveness rather than blindly chasing concepts.

Second, distinguish between cyclical fluctuations and structural changes. AI represents a technological revolution that may last for decades, and its impact is structural. In market fluctuations, it is essential to differentiate between long-term trends and short-term noise, avoiding misjudging structural opportunities as cyclical bubbles, or vice versa.

Third, pay attention to overall market reactions and systemic risks. The rise of a single company or sector does not necessarily constitute a bubble; it is necessary to assess the overall market valuation levels, capital concentration, leverage situations, and sentiment consistency. It is especially important to be alert to signs of the AI narrative shifting from "profit-supported" to "story-driven."

Fourth, make good use of analytical tools, including AI itself. AI technology can be used to more accurately assess market risks, corporate values, and economic impacts. Practitioners should actively utilize data analysis, machine learning, and other tools to enhance decision-making quality while being cautious of the new risks that may arise from model homogenization.

V. Engage with Rationality and Enthusiasm in a Continuous, Multi-Dimensional, and Dynamic Manner

Jefferson's final conclusion is relatively cautiously optimistic: based on comparisons across the four dimensions of profitability basis, valuation levels, number of companies, and financial leverage, there are significant differences between the current AI boom and the internet bubble, making the likelihood of a repeat of the severe collapse of the late 1990s relatively low. The development of AI is rooted in a number of financially sound mature enterprises, and the overall financial system is resilient.

However, uncertainties still exist. The long-term impacts of AI on employment, inflation, and productivity still require time to validate; market sentiment may reverse; the activity level in the private equity market may obscure risks; and the potential for infrastructure investments to raise leverage warrants caution. Therefore, the Federal Reserve will continue to monitor AI development to ensure it unfolds within a stable and resilient financial environment, ultimately serving the fundamental goal of maximizing employment and ensuring price stability.

For the market, Jefferson's analysis provides a toolbox for rationally assessing AI investments. In the wave of technological revolution and capital enthusiasm, maintaining clarity, distinguishing essence from appearance, and focusing on long-term value may be the best posture to avoid bubbles and embrace change. Is AI a bubble? The answer lies not in a simple yes or no, but in continuous, multi-dimensional, and dynamic observation and judgment.

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