The initial AI boom was driven by experimentation and signaling. Now, CFOs are demanding measurable returns, which most companies aren't seeing. This shift from the 'experimental era' to the 'ROI era' will likely cause a significant drawdown in the valuations of overhyped AI stocks.

The key volatility risk for the AI trend is a potential "air pocket"—a timing gap between the massive CapEx on training infrastructure and the actual realization of productivity gains from AI applications. This handoff period could trigger a market correction, even for long-term believers in AI.

A temporary mismatch is emerging in the AI sector where massive capital investment in compute is running ahead of widespread monetization. This could create an 'air gap' around 2027 where quarterly-focused investors panic, offering a prime entry point for those with longer, multi-year time horizons.

Massive upfront capital expenditure (CapEx) for AI infrastructure creates a timing gap before revenue materializes. This mirrors historical bubbles like the dot-com and railroad eras, where the technology succeeded but early investors were wiped out waiting for returns.

The AI industry is exhibiting signs of a dot-com-style bubble correction. After a frenzy of investment in infrastructure (supply), delayed IPOs and strategy pivots from companies like Meta and xAI suggest that end-user demand is not materializing as quickly as projected.

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.

The dot-com crash didn't stop internet adoption; it only decimated stock values. Similarly, an "AI winter" for investors is possible even as AI technology becomes more integrated into society. Investors should distinguish between technological adoption and market valuation.

The stock market's enthusiasm for AI has created valuations based on future potential, not current reality. The average company using AI-powered products isn't yet seeing significant revenue generation or value, signaling a potential market correction.

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.