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.

When a company's valuation is based on profits projected decades into the future, it reaches a critical point. Investors eventually stop buying into even more distant projections, causing a stall as they wait for reality to catch up or sell to others who still believe.

Counter to conventional value investing wisdom, a low Price-to-Earnings (P/E) ratio is often a "value trap" that exists for a valid, negative reason. A high P/E, conversely, is a more reliable indicator that a stock may be overvalued and worth selling. This suggests avoiding cheap stocks is more important than simply finding them.

With the S&P 500's Price-to-Earnings ratio near 28 (almost double the historic average) and the Shiller P/E near 40, the stock market is priced for perfection. These high valuation levels have historically only been seen right before major market corrections, suggesting a very thin safety net for investors.

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.

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.

Current market multiples appear rich compared to history, but this view may be shortsighted. The long-term earnings potential unleashed by AI, combined with a higher-quality market composition, could make today's valuations seem artificially high ahead of a major earnings inflection.

Unlike the 2008 crisis, which was concentrated in housing and banking, today's risk is an 'everything bubble.' A decade of cheap money has simultaneously inflated stocks, real estate, crypto, and even collectibles, meaning a collapse would be far broader and more contagious.

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.