The Shiller P/E ratio, a measure of long-term market valuation, has only crossed 40 three times: 1929, 1999, and today. The first two instances preceded major market crashes (The Great Depression, Dot-com Bust) and were followed by a decade or more of flat or negative real returns for investors.

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.

Overvaluing assets in a new tech wave is common and leads to corrections, as seen with mobile and cloud. This differs from a systemic collapse, which requires fundamental weaknesses like the massive debt and fraud that fueled the dot-com crash. Today's AI buildout is funded by cash-rich companies.

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.

Today's market is more fragile than during the dot-com bubble because value is even more concentrated in a few tech giants. Ten companies now represent 40% of the S&P 500. This hyper-concentration means the failure of a single company or trend (like AI) doesn't just impact a sector; it threatens the entire global economy, removing all robustness from the system.

Different valuation models tell conflicting stories about the US market. The Shiller CAPE ratio suggests extreme overvaluation near dot-com bubble highs. However, a reverse DCF model calculating the implied equity risk premium shows the market is only moderately valued, creating a confusing picture for investors.

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.

The comparison to the dot-com bubble is incomplete. The current AI hype cycle hasn't yet been fueled by low interest rates or widespread leverage—factors that drove the final mania phase of the 1999 bubble. This suggests the market could get 'a lot crazier' before a significant correction.

The most immediate systemic risk from AI may not be mass unemployment but an unsustainable financial market bubble. Sky-high valuations of AI-related companies pose a more significant short-term threat to economic stability than the still-developing impact of AI on the job market.