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The AI Supercycle: is it a bubble? Why railways and dot-com already answered

The question everyone asks about AI is "is it a bubble, yes or no?" That is the wrong question, and the previous nine chapters explain why. Railways were a real, world-changing technology and a savage bubble. The internet was a real, world-changing technology and a savage bubble. $AMZN fell about 95% in the dot-com crash and went on to become one of the largest companies on earth. "Real" and…

The inquiry surrounding artificial intelligence prompts the question of whether it is experiencing a bubble, yet the previous chapters argue that the more pertinent question is which components of the AI supercycle represent enduring development, and which elements resemble the speculative financing schemes of past bubbles. The analogy drawn between railways and the dot-com era illustrates that real technological advancements can coexist with inflated equity, the infrastructure often outlasting the financial hype.

The current focus is not on identifying the bubble's peak but on discerning which element drives the price movement. The substantial capital expenditures towards AI data centers by the leading U.S. hyperscalers underscore the real build-out, although the return on this investment remains uncertain. This parallel to past infrastructure booms suggests that significant investments can be made with uncertain returns, as seen with railways that were later followed by bankruptcies.

The circular financing model, exemplified by Nvidia financing its own customers, creates a feedback loop where demand and valuation are intertwined, making it challenging to distinguish between sustainable growth and speculative bubbles. The key takeaway is that the AI supercycle embodies the same structural characteristics observed in previous economic bubbles, highlighting the difficulty in distinguishing between genuine technological innovation and financial speculation.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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