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.

Glenn Fogel draws parallels between the current AI hype and previous speculative booms like the dot-com era. He predicts that while many AI companies will fail and investors will lose money, the frenzy will also produce companies that create immense, lasting value, following a historical pattern of innovation.

The current AI boom follows Schumpeter's classic model of technological change: massive, credit-fueled overinvestment causes a boom. This will be followed by a bust and recession as the new technology displaces old industries and most AI firms fail. Only then will the technology fully permeate society during the subsequent slump.

The current AI spending frenzy uniquely merges elements from all major historical bubbles—real estate (data centers), technology, loose credit, and a government backstop—making a soft landing improbable. This convergence of risk factors is unprecedented.

Tech giants are spending hundreds of billions on AI infrastructure with slow initial results, reminiscent of the Web 1.0 era's overbuild of fiber optic networks. This parallel suggests a potential AI bubble where the infrastructure is built, but the equity holders who funded it get crushed in a market correction.

Investor Howard Marks notes that every major technological revolution, from railroads to the internet, has produced a money-losing bubble. The current AI excitement, characterized by the belief that "no price is too high" and that rules have changed, mirrors the psychology of past bubbles, suggesting extreme risk for investors.

Widespread credit is the common accelerant in major financial crashes, from 1929's margin loans to 2008's subprime mortgages. This same leverage that fuels rapid growth is also the "match that lights the fire" for catastrophic downturns, with today's AI ecosystem showing similar signs.

Unlike past bubbles driven by single factors like credit, tech, or real estate, the current AI moment uniquely sits at the intersection of all major historical bubble ingredients simultaneously: loose credit, a great technology story, a real estate component (data centers), and a policy angle.

Unlike previous tech cycles, the current AI expansion relies heavily on cheap debt financing by hyperscalers. A credit market crisis, potentially triggered by geopolitical instability, could choke off this funding and cause a sharp, widespread correction in the AI sector.