The dot-com era was not fueled by pure naivete. Many investors and professionals were fully aware that valuations were disconnected from reality. The prevailing strategy was to participate in the mania with the belief that they could sell to a "greater fool" before the inevitable bubble popped.
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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.
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.
During the dot-com bubble, Howard Marks used second-order thinking to stay rational. Instead of asking which tech stocks were innovative (a first-order question), he asked what would happen *after* everyone else piled in. This focus on embedded expectations, rather than simple quality, is key to avoiding overpriced, crowded trades.
A market enters a bubble when its price, in real terms, exceeds its long-term trend by two standard deviations. Historically, this signals a period of further gains, but these "in-bubble" profits are almost always given back in the subsequent crash, making it a predictable trap.
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.
Marks emphasizes that he correctly identified the dot-com and subprime mortgage bubbles without being an expert in the underlying assets. His value came from observing the "folly" in investor behavior and the erosion of risk aversion, suggesting market psychology is more critical than domain knowledge for spotting bubbles.
The dot-com bubble didn't create wealth in 1999; it destroyed it. Generational wealth came from buying and holding survivors like Amazon *after* its stock had fallen 95%. The winning strategy isn't timing the crash, but surviving it and holding quality assets through the long recovery.
A macro strategist recalls dot-com era pitches justifying valuations with absurd scenarios like pets needing cell phones or a company's tech being understood by only three people. This level of extreme mania highlights a key difference from today's market, suggesting current hype levels are not unprecedented.
A market isn't in a bubble just because some assets are expensive. According to Cliff Asness, a true bubble requires two conditions: a large number of stocks are overvalued, and their prices cannot be justified under any reasonable financial model, eliminating plausible high-growth scenarios.
Marks argues that speculative bubbles form around 'something new' where imagination is untethered from reality. The AI boom, like the dot-com era, is based on a novel, transformative technology. This differs from past manias centered on established companies (Nifty 50) or financial engineering (subprime mortgages), making it prone to similar flights of fancy.
