Today's massive AI company valuations are based on market sentiment ("vibes") and debt-fueled speculation, not fundamentals, just like the 1999 internet bubble. The market will likely crash when confidence breaks, long before AI's full potential is realized, wiping out many companies but creating immense wealth for those holding the survivors.

Following George Soros's theory of reflexivity, markets act like thermostats, not barometers. Rising AI stock prices attract capital, which further drives up prices, creating a self-reinforcing loop. This feedback mechanism detaches asset values from underlying business fundamentals, inflating a bubble based on pure belief.

Current AI investment patterns mirror the "round-tripping" seen in the late '90s tech bubble. For example, NVIDIA invests billions in a startup like OpenAI, which then uses that capital to purchase NVIDIA chips. This creates an illusion of demand and inflated valuations, masking the lack of real, external customer revenue.

Widespread public debate about whether a market is in a bubble is evidence that it is not. A true financial bubble requires capitulation, where nearly everyone believes the high valuations are justified and the skepticism disappears. As long as there are many vocal doubters, the market has not reached the euphoric peak that precedes a crash.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

Contrary to intuition, widespread fear and discussion of a market bubble often precede a final, insane surge upward. The real crash tends to happen later, when the consensus shifts to believing in a 'new economic model.' This highlights a key psychological dynamic of market cycles where peak anxiety doesn't signal an immediate top.

In a late-stage bubble, investor expectations are so high that even flawless financial results, like Nvidia's record-breaking revenue, fail to boost the stock price. This disconnect signals that market sentiment is saturated and fragile, responding more to narrative than fundamentals.

Financial models struggle to project sustained high growth rates (>30% YoY). Analysts naturally revert to the mean, causing them to undervalue companies that defy this and maintain high growth for years, creating an opportunity for investors who spot this persistence.

While AI investment has exploded, US productivity has barely risen. Valuations are priced as if a societal transformation is complete, yet 95% of GenAI pilots fail to positively impact company P&Ls. This gap between market expectation and real-world economic benefit creates systemic risk.