Is the AI Boom a Bubble? 6 Counterintuitive Truths About Tech Hype

1.0 Introduction: The Déjà Vu of the AI Boom

It’s impossible to ignore. The current boom in Artificial Intelligence has everyone talking, with breathless excitement about the future mixing with a deep-seated anxiety about the present. For many, this moment feels eerily familiar, constantly drawing comparisons to the dot-com bubble of the late 1990s. The question on everyone's mind is a simple one: Are we doomed to repeat history, watching another spectacular bubble inflate only to burst, or is something fundamentally different happening this time?

Before we rush to a conclusion, it’s worth challenging our common assumptions. The story of technological booms and busts is more complex—and far more interesting—than we think. Here are six surprising and counter-intuitive truths about tech bubbles that can help us make sense of the current AI frenzy.

2.0 Takeaway 1: Hype Isn't Just Irrational—It's a Historical Engine for Progress

We tend to view financial bubbles as collective mistakes driven by irrational greed. However, the work of economist Carlota Perez suggests a different perspective: massive technological revolutions, which she argues occur every 50 to 60 years, are consistently powered by speculative financial cycles.

According to Perez's model, each great technological surge enters a "Frenzy phase." During this period, financial capital decouples from the actual production of goods and services, and investors pour money into new technologies based on future promise, inflating massive asset bubbles. This decoupling is amplified in our current era, where intangible assets like data and algorithms account for 90% of a company's value, making traditional valuation metrics almost irrelevant during a frenzy.

While this phase is chaotic and often ends in a crash, it's not just a bug in the system—it's a crucial feature. This speculative frenzy finances the enormous, expensive infrastructure build-out that the new technology requires to become mainstream. Think of the canal manias of the industrial revolution or the frantic laying of fiber-optic cables during the dot-com boom. Much of that investment was lost, but it left behind the essential infrastructure for the next era of growth. From this historical lens, bubbles are a messy but necessary mechanism for funding progress on a scale that cautious, "rational" investment never could.

But if these bubbles are a recurring feature of technological progress, what is the universal human behavior that powers them? This brings us to the individual investor's mind.

3.0 Takeaway 2: The Famous 'Hype Cycle' Chart Is More Myth Than Reality

Anyone who has followed technology for a while has seen the Gartner Hype Cycle. It presents a neat, predictable rollercoaster ride for new technologies: from the "Technology Trigger" to the "Peak of Inflated Expectations," down into the "Trough of Disillusionment," and finally climbing the "Slope of Enlightenment" to the "Plateau of Productivity." It’s a compelling story that seems to capture our collective experience with new tech.

There’s just one problem: it's not very accurate. Many critics have long argued that the model isn’t scientific. They point out that it is not truly a "cycle," offers no actionable perspective for how to advance a technology, and uses subjective terms like "disillusionment" that cannot be objectively measured. A 2024 analysis by The Economist that reviewed the model's historical track record delivered a particularly damning verdict on its predictive power. It found that the journey through the trough is often a final destination.

"We estimate that of all the forms of tech which fall into the trough of disillusionment, six in ten do not rise again."

The analysis concluded that real-world data shows only a small fraction of technologies—perhaps a fifth—actually follow this clean narrative arc. The Hype Cycle is a popular and intuitive story, but we should be extremely cautious about using it as a map to predict the future. For strategists, this means the Hype Cycle should be treated as a tool for understanding market sentiment, not as a reliable map for allocating capital.

4.0 Takeaway 3: The 'Fear of Missing Out' Is a Near-Universal Trap (and It Has a Surprising Cause)

The "Fear of Missing Out" (FOMO) is often dismissed as a rookie investor's mistake, but empirical research reveals it's a powerful and nearly universal psychological phenomenon. A study of active retail traders by Guohua Wu found that a staggering 96.99% of them admitted to experiencing the complete "observe–hesitate–missed-out–chase-high" FOMO cycle.

The reason this trap is so effective lies in a sophisticated psychological mechanism called "time-asymmetric regret aversion." It’s a constant battle between two different types of fear:

1. Early in a trend, we are paralyzed by the fear of commission errors. This is the regret we feel from taking an action (like buying a stock) and being wrong. To avoid this sharp, immediate pain, we hesitate and stay on the sidelines.
2. Late in a trend, we are driven by the fear of omission errors. This is the nagging, growing regret of not acting and missing a massive opportunity. As we see others profiting, this feeling is amplified by social pressure, eventually becoming so painful that we jump in at any price just to make it stop.

This internal tug-of-war between two distinct types of regret is the core engine of the "buy high" behavior that inflates and sustains asset bubbles. This internal battle with regret creates the perfect psychological fuel for what economists call the "Greater Fool Theory," a dynamic we'll explore further.

5.0 Takeaway 4: We're Trying to Predict the AI Future Using a Broken Dot-Com Playbook

The most common way to analyze the AI boom is to compare it to the dot-com bubble. But what if that entire playbook is broken? A 2024 research paper by Aksheytha Chelikavada and Casey C. Bennett did a deep, data-driven comparison of the two eras, analyzing scientific publishing activity and financial markets to see if the patterns from the 1990s could predict today's events.

Their core finding was unambiguous: patterns observed in scientific activity and citation networks during the dot-com era do not effectively translate or predict market behavior in the current AI era. However, they uncovered a crucial nuance: a small but significant subset of AI scientists are exhibiting influence patterns that mirror those of key figures during the dot-com hype. While this is not enough to validate the old playbook, it may be an early, faint signal of hype building in a new form.

Ultimately, after running their models, they were left with two starkly different possibilities: either the current AI era is shaping up to be an "unprecedented form of financial bubble unseen" or, simply, "no bubble exists." The crucial takeaway is that treating the AI boom as a simple sequel to the dot-com story is a deeply flawed analytical approach.

6.0 Takeaway 5: Why the Old Playbook Is Broken: Intangible Value and Crumbling Moats

If the old models are failing, we need to understand why. Research from Weitao Gan into value investing in the digital age provides two clear reasons.

First, old money metrics don't work. Traditional valuation ratios like price-to-earnings (P/E) were designed for an industrial economy where value resided in tangible assets like factories and inventory. Today, that's no longer the case. Intangible assets—such as data, brand equity, and proprietary algorithms—now account for approximately 90% of the total value of S&P 500 companies. When Amazon spent years posting low profits to build out its AWS cloud infrastructure, traditional metrics saw a struggling retailer, not a company building a massive, long-term value engine.

Second, competitive "moats" are draining fast. Warren Buffett’s famous "moat" theory states that the best companies have durable competitive advantages that protect them from rivals. But in the digital age, the rapid pace of technological iteration makes these defenses far more vulnerable. The vulnerability of these moats is reflected in corporate lifespans; the average tenure of a company on the S&P 500 has plummeted from 60 years in the 1960s to less than 20 years today. Kodak owned the patents for film photography but couldn't adapt to the digital disruption it helped create. Intel, long the dominant chipmaker, fell behind TSMC in process technology. For investors, this signals that a "buy and hold" strategy based on a company's past dominance is riskier than ever; continuous assessment of a firm's adaptive capacity is now paramount.

7.0 Takeaway 6: It's Not a Bubble, It's a Search for the Next 'Greater Fool'

Perhaps the simplest and most powerful explanation for bubble dynamics is the "Greater Fool Theory." The theory states that you can profit from buying an overvalued asset as long as you can find someone else—a "greater fool"—willing to buy it from you at an even higher price.

This process feeds on itself, driven by herd mentality and the fear of missing out. It continues until the market simply runs out of new fools willing to pay increasingly absurd prices. At that point, the bubble bursts, and the price crashes back toward its intrinsic value. This dynamic helps explain behavior in markets ranging from art and real estate to cryptocurrencies and tech stocks, where prices can become completely detached from fundamentals. As economist Burton Malkiel explains in his classic book, A Random Walk Down Wall Street:

"A bubble starts when any group of stocks... begin to rise. The updraft encourages more people to buy the stocks... which pulls in larger and larger groups of investors. But the whole mechanism is a kind of Ponzi scheme where more and more credulous investors must be found to buy the stock from the earlier investors. Eventually, one runs out of greater fools."

8.0 Conclusion: Asking a Better Question

Trying to definitively label the AI boom as a bubble by simply comparing it to the past is likely the wrong approach. The underlying rules of value creation have been rewritten by intangible assets, the psychological drivers of FOMO are more powerful than ever, and historical patterns may no longer apply.

Instead of asking, "Are we in a bubble?", perhaps the more important question is, "Are we building the new rules, valuation models, and mental resilience needed to navigate a world that moves this fast?"