The AI craze and the dot-com bubble: echoes across two eras
The surge of enthusiasm surrounding artificial intelligence in today’s equity markets bears unmistakable similarities to the dot-com boom of 2000–2001.
In both eras, investors became enthralled by a transformative technology that promised to reshape entire industries. In the late 1990s, the internet was expected to reinvent commerce, communication, and business models. Today, generative AI inspires comparable expectations, seen as a force capable of reshaping productivity, decision-making, and automation. The shared narrative is powerful: early adopters and infrastructure providers will dominate the future, and investors scramble to identify potential winners before the rest of the market catches up.
Market behavior reflects this optimism. During the dot-com boom, internet-linked companies achieved extraordinary valuations regardless of earnings, cash flow, or balance-sheet quality. A similar dynamic is unfolding today: chipmakers, cloud platforms, and datacenter developers enjoyed outsized price appreciation. As in 1999, investor psychology gravitates toward long-term total addressable markets rather than near-term fundamentals. Where the dot-com era focused on novel metrics such as “eyeballs” and “page views,” the current AI cycle leans on GPU demand, model-training scale, and speculative AI-related revenue projections. In both periods, companies are valued less for what they are and more for what they might become.
The historical parallel, however, contains an important lesson. The dot-com bubble ultimately burst when expectations decoupled too far from economic reality. Technological innovation proved real, but profitability, competitive dynamics, and capital intensity still mattered. Even before the collapse, companies themselves signaled rising risks: regulatory filings grew longer, denser, and more technical. As enthusiasm soared, the public narrative became simpler and more promotional, while the legal and accounting disclosures quietly expanded. Subsequent academic research confirmed the pattern: less readable, more convoluted filings were statistically associated with higher stock-price crash risk.
A similar disclosure divergence is emerging today, particularly among the datacenter developers and AI-infrastructure plays – like CoreWeave, TerraWulf or Cipher Mining. These firms market crisp narratives centered on “AI cloud,” “next-generation compute,” or “high-efficiency mining.” Yet their regulatory filings tell a more complicated story. Behind the simplicity of the equity pitch lie sprawling pages of caveats, obligations, contractual dependencies, and concentration risks. CoreWeave’s most recent quarterly report, for example, devotes roughly 45 pages to risk disclosures alone—striking for a company still early in its public-market life.
The broader message is familiar. When technological revolutions ignite investor imagination, disclosure complexity often rises in parallel—sometimes because the underlying business is genuinely intricate, and sometimes because risks are mounting faster than the narrative allows. The dot-com era taught investors that technological promise does not eliminate financial discipline. The AI era may prove no different.
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