The severity of the dot-com crash was so profound that in 1999, a venture capital fund that simply returned its investors' initial capital (a 1x return) was considered a top-quartile performer. This historical benchmark puts the scale of that market collapse and the subsequent struggle for VCs into stark perspective.

Jeff Aronson warns that prolonged success breeds dangerous overconfidence. When an investor is on a hot streak and feels they can do no wrong, their perception of risk becomes warped. This psychological shift, where they think "I must be good," is precisely when underlying risk is escalating, not diminishing.

Simply keeping pace with peers is not a valid measure of success. If peers are taking excessive risks in a bubble, matching their performance means you were equally foolish. True skill is outperforming in bad times while keeping pace in good times.

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.

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.

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.

During the dot-com era, savvy investors recognized they were in a bubble but termed it an "iron bubble," believing it would persist. Bailing out too early was a greater risk than riding it to the end, as it meant missing out on significant late-stage gains. This mindset is relevant for navigating today's AI boom.

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.