The rise of artificial intelligence has sparked a gloomy renaissance thought of past economic bubbles, including the notorious ‘Dot-Com’ crash of the late 1990s. Bubbles form when innovation fuels speculative investment, driving asset prices far beyond inherent value — a phenomenon that has reoccurred throughout history, from the ‘Tulip Mania’ in the 17th century to the financial crisis two decades ago. Today, the AI boom is changing services and attracting billions in venture capital, but it also raises critical questions about whether we are on the edge of another speculative collapse.
Economic Bubbles as Theoretic Concept
Economic bubbles occur when the price of assets inflates rapidly due to excessive speculation, overtaking their intrinsic value, only to collapse sharply when reality sets in (Girdzijauskas et al. 2009). These bubbles typically form in stages: an exciting innovation triggers initial enthusiasm, followed by a speculative turmoil where investors pour money into overhyped ventures due to inflated expectations (Chang et al. 2016). Eventually, prices peak and crash, leaving economic consequences as disillusionment unfolds (Girdzijauskas et al. 2009). Finally, the enlightenment leaves many investors nursing losses, and the economy adjusts as capital reallocates to more stable opportunities as productivity sets in (Chang et al. 2016) Figure 1 shows this cycle.
Fig. 1: Gartner Hype Cycle (Fenn and Raskino, 2008), own adaptation
Like the internet boom of the late 1990s, AI is revolutionizing technology, but questions remain about whether current investments reflect genuine value or inflated expectations.
Lessons from Economic History
These patterns can be observed throughout historical bubbles, where the most prominent ones include the ‘Tulip Mania’ in the 17th century and financial crisis in the 21st century. The Tulip Mania unfolded in the Netherlands in the 17th century and occurred as contracts for tulip bulbs were traded before the growing season ended (Thompson, 2007). At that time, tulips became a status symbol among wealthy traders in Western Europe (Thompson, 2007). Tulips could multiply over time, allowing investors to profit by trading newly produced bulbs, further fueling speculation in what many viewed as a lucrative investment opportunity (Garber, 1989). In similar manner, the 2007 financial crisis was the largest economic downturn since World War II. It began in the U.S. due to a housing bubble fueled by easy access to credit, where mortgage approvals were granted without rigorous checks on applicants (Flannery, Kwan & Nimalendran, 2013). As more and more homeowners avoided their loans and housing prices stagnated, banks faced cash shortages because mortgage rates remained unchanged while resale became difficult (Mian & Sufi, 2008). Over time, banks struggled to lend or borrow funds, leading to widespread toxic debts and bank closures. The crisis spread globally, particularly affecting Western economies with similar banking systems, as they faced a stark effect from the U.S. financial collapse. (Mian & Sufi, 2008).
In the technical sector, we can observe a similar case: The Dot-Com bubble, spanning roughly from 1995 to 2000, was a critical incident in economic history driven by the rapid rise of internet-based businesses. As the internet gained mainstream attention, it was claimed as a revolutionary technology (Wheale & Amin, 2003). This optimism led to an explosion of tech startups, many of which rushed to go public despite having unproven business models or nonexistent profits (Morris & Alam, 2012). The Nasdaq index, heavily weighted toward technology stocks, surged nearly 400% during this period, peaking in March 2000 (Wheale & Amin, 2003). However, as investors began scrutinizing these companies’ financial data, the bubble burst, erasing nearly $5 trillion in market value over the following years (Morris & Alam, 2012).
All these burst bubbles teach us fundamental economic lessons. First, “irrational exuberance” (syn: excitement), a term popularized by economist Alan Greenspan, can lead to speculative overvaluation when investors chase trends without analyzing underlying details (Shefrin, 1999). Second, the bubbles underscored the collective misallocation of capital, as vast sums were poured into trendy ventures rather than sustainable, long-term innovations (Wonglimpiyarat & Tripipatkul, 2017).
The Stars of the Current Global AI Boom
In early 2023, the New York Times reported “Now OpenAI is in the midst of a new gold rush!“ (Griffith & Metz, 2023). As of November 2024, OpenAI is valued at approximately $157 billion following a significant funding round that raised $6.6 billion. This valuation nearly doubles its earlier valuation of $86 billion earlier in the year. The funding round was supported by major investors, including Microsoft, Softbank and Nvidia. (For details: Reuters)
Nvidia’s stock has shown consistent growth, primarily driven by demand for its AI, data center, and GPU technologies. As of November 2024, the company’s stock price reflects a year-to-date increase of 176% and a 12-month rise of 186%. This performance coincides with Nvidia’s Q3 2024 revenue of $35.1 billion, a 94% increase from the previous year, supported by $30.8 billion in data center revenues. Nvidia’s market capitalization remains above $1 trillion, reflecting its powerful position in advancing AI infrastructure and computational technologies. (For details: NVIDIA Corporation (NVDA) stock price)
Outlook
The current AI hype shares notable similarities to past bubbles, and this is something we should not overlook. While not all indicators suggest an imminent crash, the parallels are striking and warrant caution. NVIDIA has experienced explosive stock growth due to its dominance in GPU production (critical for AI and machine learning) while OpenAI’s breakthroughs in language models like ChatGPT have fueled widespread enthusiasm about AI’s transformative potential. However, much like Tulip Mania, some valuations seem detached from profitability. NVIDIA’s future depends on the broad adoption of AI-powered applications across industries, while OpenAI’s revenue models, such as API subscriptions, may not yet justify its perceived market potential.
Learning from the Dot-Com bubble, the challenge for AI companies lies in proving long-term value. Many startups lack feasible business models, mirroring the failures of unprofitable Dot-Com ventures, and investors’ assumptions of boundless market growth risk leading to overvaluation. Moreover, systemic risks, as demonstrated by the 2007 financial crisis, underline how interconnected markets can amplify the impact of a bubble collapse. Overinvestment in AI without understanding its limitations could stress companies, while the failure of venture-backed startups could cascade into broader investor losses.
As the stories of Tulip Mania, the Dot-Com bubble, and the financial crisis remind us, rationality is essential in navigating markets driven by innovation and hype. While grounded in genuine technological advancements, the AI hype reflects critical elements of past economic bubbles. To avoid a repeat of history, investors, companies, and regulators need to act with caution, ensuring that speculative excesses do not undermine sustainable growth.
Jennifer-Marieclaire Sturlese
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