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.

History shows a pattern: the 2022 "Crypto Bowl" preceded a crash, and the 2000 "Internet Bowl" preceded the dot-com bust. A flood of ads from one sector, like AI this year, suggests companies are urgently trying to monetize massive investments, indicating a potential bubble.

The current AI spending spree by tech giants is historically reminiscent of the railroad and fiber-optic bubbles. These eras saw massive, redundant capital investment based on technological promise, which ultimately led to a crash when it became clear customers weren't willing to pay for the resulting products.

The proliferation of billboards for highly specialized, unintelligible B2B companies along Silicon Valley's Highway 101 signals market froth. When advertising shifts from consumer brands to obscure B2B2B services, it suggests excess capital is flowing deep into the tech stack, a classic sign of a potential bubble.

Mark Cuban argues the AI bubble isn't in public markets like the dot-com era. Instead, it's the unsustainable, winner-take-all spending race between a few large companies building foundational models. This creates an opportunity for disruption by more efficient technologies.

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.

The current AI-driven CapEx cycle is analogous to historical bubbles like the 19th-century railroad buildout and the dot-com boom. These periods of intense capital investment have historically led to major economic downturns and secular bear markets, suggesting a grim multi-year outlook beyond the current cycle.

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.

Historical technology cycles suggest that the AI sector will almost certainly face a 'trough of disillusionment.' This occurs when massive capital expenditure fails to produce satisfactory short-term returns or adoption rates, leading to a market correction. The expert would be 'shocked' if this cycle avoided it.