The AI market is a “classic bubble“ that is approaching a point of bursting, Bridgewater Associates founder Ray Dalio said at the Forbes Global CEO conference in Singapore. According to him, the risk is increased by rising interest rates and the need to turn accumulated wealth into cash.
“We are in the part of the cycle that precedes the bursting of the bubble, but we are getting close to it. I think we are close“, Dalio said.
The investor pointed out that huge amounts of debt are being raised to finance AI. According to him, if interest rates continue to rise, the cost of this financing will increase and the pressure on companies and investors will increase.
The need for available funds increases the risk
Dalio also identified as a possible catalyst the moment when asset owners have to sell some of them to get cash. He linked this risk to wealth taxes and other measures that can turn unrealized gains into real sales.
„Everyone says: „I have a billion dollars“, but try spending it. To use that money, you have to sell your wealth and then the bubble usually bursts,“ Dalio said.The warning comes against the backdrop of large-scale investments in artificial intelligence infrastructure. Tech giants are pouring hundreds of billions of dollars into data centers, chips and computing power, some of which is financed through debt. At the same time, global bond yields have reached their highest levels in decades, making investments more expensive.
Index gains concentrated in a few companies
The S&P 500 and Nasdaq 100 indexes hit record highs this week, but the gains were concentrated in a limited number of companies related to artificial intelligence. Bank of America strategists warned that the low number of stocks that drive the market up is characteristic of periods when bubbles form.
According to Bank of America, US technology stocks have the highest value in the bank's bubble risk indicator among the asset classes it monitors. This is not a forecast for a specific date of decline, but an assessment of increasing vulnerability to continued stock price appreciation and high financing costs.
Separately, McKinsey estimates that by 2030, global spending on building data centers could reach $7 trillion. This volume of investment would increase pressure on the market if productivity growth and AI revenues do not justify the invested funds.
Sources: rbc.ru, economictimes.indiatimes.com, finance.yahoo.com