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.
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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.
Phenomena like bank runs or speculative bubbles are often rational responses to perceived common knowledge. People act not on an asset's fundamental value, but on their prediction of how others will act, who are in turn predicting others' actions. This creates self-fulfilling prophecies.
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.
Innovation doesn't happen without risk-taking. What we call speculation is the essential fuel that allows groundbreaking ideas, like those of Elon Musk, to get funded and developed. While dangerous, attempting to eliminate speculative bubbles entirely would also stifle world-changing progress.
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.
A clear statement from a financial leader like the Fed Chair can instantly create common knowledge, leading to market movements based on speculation about others' reactions. Alan Greenspan's infamous "mumbling" was a strategic choice to avoid this, preventing a cycle of self-fulfilling expectations.
Current AI spending appears bubble-like, but it's not propping up unprofitable operations. Inference is already profitable. The immense cash burn is a deliberate, forward-looking investment in developing future, more powerful models, not a sign of a failing business model. This re-frames the financial risk.
The AI market won't just pop; it will unwind in a specific sequence. Traditional companies will first scale back AI investment, which reveals OpenAI's inability to fund massive chip purchases. This craters NVIDIA's stock, triggering a multi-trillion-dollar market destruction and leading to a broader economic recession.
Michael Burry, known for predicting the 2008 crash, argues the AI bubble isn't about the technology's potential but about the massive capital expenditure on infrastructure (chips, data centers) that he believes far outpaces actual end-user demand and economic utility.
