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.

Stock market investors are pricing in rapid, significant productivity gains from AI to justify high valuations. This sets up a binary outcome: either investors are correct, leading to massive productivity growth that could disrupt the job market, or they are wrong, resulting in a painful stock market correction when those gains fail to materialize.

Unlike the 1990s tech bubble, today's companies have higher net margins (14% vs. 8%) and better cash flow. This, combined with a rare mix of monetary easing, fiscal stimulus, and deregulation outside of a recession, makes current equity multiples look more reasonable.

Blinder asserts that while AI is a genuine technological revolution, historical parallels (autos, PCs) show such transformations are always accompanied by speculative bubbles. He argues it would be contrary to history if this wasn't the case, suggesting a major market correction and corporate shakeout is inevitable.

History shows that transformative innovations like airlines, vaccines, and PCs, while beneficial to society, often fail to create sustained, concentrated shareholder value as they become commoditized. This suggests the massive valuations in AI may be misplaced, with the technology's benefits accruing more to users than investors in the long run.

Current market multiples appear rich compared to history, but this view may be shortsighted. The long-term earnings potential unleashed by AI, combined with a higher-quality market composition, could make today's valuations seem artificially high ahead of a major earnings inflection.

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.

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.

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.