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.
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While AI technology will achieve widespread adoption and major breakthroughs, the financial infrastructure supporting it will falter. Peripheral companies that jumped on the AI trend without a core business will face a significant market correction, creating a paradoxical "best and worst" year for the industry.
Like the dot-com era, many overvalued AI startups will fail. However, this is distinct from the underlying technology. Artificial intelligence itself is a fundamental, irreversible shift that will permanently change the world, similar to how the internet and social media became globally dominant despite early market bubbles.
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 that transformative technologies like railroads and the internet often create market bubbles. Investors can lose tremendous amounts of capital on overpriced assets, even while the technology itself fundamentally rewires the economy and creates massive societal value. The two outcomes are not mutually exclusive.
Overvaluing assets in a new tech wave is common and leads to corrections, as seen with mobile and cloud. This differs from a systemic collapse, which requires fundamental weaknesses like the massive debt and fraud that fueled the dot-com crash. Today's AI buildout is funded by cash-rich companies.
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.
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.
The current AI boom may not be a "quantity" bubble, as the need for data centers is real. However, it's likely a "price" bubble with unrealistic valuations. Similar to the dot-com bust, early investors may unwittingly subsidize the long-term technology shift, facing poor returns despite the infrastructure's ultimate utility and value.
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.
Unlike the 2008 financial crisis, which was a debt-fueled credit unwind, the current AI boom is largely funded by equity and corporate cash. Therefore, a potential correction will likely be an equity unwind, where the stock prices of major tech companies fall, impacting portfolios directly rather than triggering a systemic credit collapse.
