Contrary to claims of an AI bubble, the market is demonstrating rationality by punishing companies like Oracle and Broadcom for failing to meet AI-related expectations. This selective valuation indicates a discerning market that rewards performance over hype, not an indiscriminate bubble where any 'AI' stock soars.
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Current AI-driven equity valuations are not a repeat of the 1990s dot-com bubble because of fundamentally stronger companies. Today's major index components have net margins around 14%, compared to just 8% during the 90s bubble. This superior profitability and cash flow, along with a favorable policy backdrop, supports higher multiples.
For the past 18 months, AI excitement has created a rising tide that boosted fortunes for all major tech companies. This is changing. In the next year, their strategic bets, investments, and results will diverge dramatically, revealing clear winners and losers as "the tide goes out for some people."
A true bubble, like the dot-com crash, involves stock prices falling over 50% and staying depressed for years, with capital infusion dropping similarly. Short-term market corrections don't meet this historical definition. The current AI boom, despite frothiness, doesn't exhibit these signs yet.
The current AI boom is more fundamentally sound than past tech bubbles. Tech sector earnings are greater than capital expenditures, and investments are not primarily debt-financed. The leading companies are well-capitalized with committed founders, suggesting the technology's endurance even if some valuations prove frothy.
David Kostin argues public AI stocks aren't in a bubble because earnings growth has matched price increases. The real bubble is in private markets, driven by George Soros's "reflexivity" theory, where a recursive loop of new capital inflates valuations to unsustainable levels.
The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.
This AI cycle is distinct from the dot-com bubble because its leaders generate massive free cash flow, buy back stock, and pay dividends. This financial strength contrasts sharply with the pre-revenue, unprofitable companies that fueled the 1999 market, suggesting a more stable, if exuberant, foundation.
The risk of an AI bubble bursting is a long-term, multi-year concern, not an imminent threat. The current phase is about massive infrastructure buildout by cash-rich giants, similar to the early 1990s fiber optic boom. The “moment of truth” regarding profitability and a potential bust is likely years away.
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.
Unlike the dot-com bubble, which was fueled by widespread, leveraged participation from retail investors and employees, the current AI boom is primarily funded by large corporations. A downturn would thus be a contained corporate issue, not a systemic economic crisis that triggers a deep, society-wide recession.