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.

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.

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.

Despite a massive tech stock run-up, key sentiment indicators and surveys of major asset allocators show caution, not the extreme bullishness seen in bubbles like the dot-com era. This suggests the market may not be at its absolute peak 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.

A condition called "fiscal dominance," where massive government debt exists, prevents the central bank from raising interest rates to cool speculation. This forces a flood of cheap money into the market, which seeks high returns in narrative-driven assets like AI because safer options can't keep pace with inflation.

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.

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.

While AI investment has exploded, US productivity has barely risen. Valuations are priced as if a societal transformation is complete, yet 95% of GenAI pilots fail to positively impact company P&Ls. This gap between market expectation and real-world economic benefit creates systemic risk.