Pundits who were correct about past tech bubbles (like crypto) are now making confidently wrong predictions about AI. This "Gell-Mann Amnesia" effect, where expertise doesn't transfer between domains, creates confusing paradoxes and forces readers to question the credibility of sources opining outside their core expertise.
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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 2022 study by the Forecasting Research Institute has been reviewed, revealing that top forecasters and AI experts significantly underestimated AI advancements. They assigned single-digit odds to breakthroughs that occurred within two years, proving we are consistently behind the curve in our predictions.
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.
With past shifts like the internet or mobile, we understood the physical constraints (e.g., modem speeds, battery life). With generative AI, we lack a theoretical understanding of its scaling potential, making it impossible to forecast its ultimate capabilities beyond "vibes-based" guesses from experts.
In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.
A genuine technological wave, like AI, creates rapid wealth, which inherently attracts speculators. Therefore, bubble-like behavior is a predictable side effect of a real revolution, not proof that the underlying technology is fake. The two phenomena come together as a pair.
A leading AI expert, Paul Roetzer, reflects that in 2016 he wrongly predicted rapid, widespread AI adoption by 2020. He was wrong about the timeline but found he had actually underestimated AI's eventual transformative effect on business, society, and the economy.
Many tech professionals claim to believe AGI is a decade away, yet their daily actions—building minor 'dopamine reward' apps rather than preparing for a societal shift—reveal a profound disconnect. This 'preference falsification' suggests a gap between intellectual belief and actual behavioral change, questioning the conviction behind the 10-year timeline.
Vanguard's Joe Davis finds that Silicon Valley insiders see a 100% chance of an AI boom, while prominent academics are equally certain of a deficit-driven slump. This polarization at the extremes suggests the moderate, consensus economic view is the least likely future.
Michael Burry, known for predicting the 2008 crash, argues the AI bubble isn't about the technology's potential but about the massive capital expenditure on infrastructure (chips, data centers) that he believes far outpaces actual end-user demand and economic utility.
