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A brutal sell-off for AI stocks sends Wall Street reeling, and the S&P 500 tumbles 4.4%

A brutal sell-off for AI stocks sends Wall Street reeling, and the S&P 500 tumbles 4.4%

A disastrous sell-off hit Wall Street on Thursday, November 5, as stocks linked to AI data centers took a severe downturn. This sharp decline erased the market's gains from the previous day, which had been the best in three months. The S&P 500 plummeted 4.4 percent, marking its worst day since the "Liberation Day" tariffs were announced by former President Donald Trump in April 2025.

The Dow Jones Industrial Average and Nasdaq composite also suffered significant losses, falling 3.4 percent and 5.5 percent, respectively. The Nasdaq, heavily weighted with AI-related and other tech stocks, fell 11.9 percent below its all-time high. Analysts consider a loss of more than 10 percent a "correction." Global markets faced pressure from the continued decline in AI stocks, with South Korea's Kospi index dropping 5.3 percent.

Critics argue that AI stock prices were inflated and were in a bubble. The recent US midterm elections added uncertainty, as Democrats' victories raised concerns about potential government-imposed pauses in AI data center construction. This could lead to fewer AI chip purchases, electricity, and related services in demand, potentially slowing the US economy's growth.

Chipmaker Nvidia, the face of the AI boom, fell 9.3 percent, while Advanced Micro Devices and CoreWeave also saw substantial declines. The S&P 500 ended the day at 7,353.73, down 334.89 points, while the Dow Jones and Nasdaq Composite also experienced significant drops. Oil prices rose slightly, and Treasury yields continued to fall, signaling investor flight to safety in US bonds.

Despite positive economic indicators, such as falling unemployment claims and improved worker productivity, investors remain cautious amid geopolitical tensions and inflation concerns.

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

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