Investors try to regain their footing after tech stocks tank

Major U.S. technology indexes steadied after one of their sharpest sell-offs in years. The Nasdaq 100, which is heavily weighted toward large technology companies, opened 1.6% higher Friday after dropping 6.3% Thursday. The S&P 500 also recovered slightly, opening 0.3% higher after losing 4.4% the previous day.

The immediate rebound reflected investors reassessing whether Thursday’s decline was excessive. Some analysts viewed it as a correction in an overstretched market, rather than proof that AI investment had ended. Economist Ed Yardeni called the declines “just speed bumps” that could prevent an AI bubble from becoming larger.

The recovery did not remove the underlying uncertainty. Investors were still weighing whether newly elected lawmakers might delay or cancel data-center projects. Volatility could continue until markets have a clearer view of how political scrutiny will affect AI infrastructure, company profits, and future valuations.

The Nasdaq 100 and S&P 500 are stock indexes, or baskets used to summarize market performance. The Nasdaq 100 focuses on 100 large nonfinancial companies listed on the Nasdaq exchange. It has a strong technology and growth-company concentration. The S&P 500 tracks roughly 500 large U.S. companies across technology, finance, health care, industry, consumer businesses, and other sectors.

That difference matters because the indexes respond to different forces. In this article, the Nasdaq 100 fell 6.3% Thursday, reflecting intense concern about the AI investment cycle. The broader S&P 500 dropped 4.4%, showing that the fear spread beyond technology but was less concentrated.

The indexes also provide different market signals. A sharp Nasdaq 100 move can indicate changing expectations for technology earnings and AI spending. A move in the S&P 500 gives a wider view of investor confidence across large U.S. companies. Both indexes rebounded Friday, but uncertainty remained.

The sell-off was large in both percentage and dollar terms. The Nasdaq 100 plunged 6.3% Thursday, erasing $2.3 billion in market value as reported in the article. The S&P 500 fell 4.4%, wiping out $2.8 trillion in paper wealth. These losses showed how heavily markets had come to depend on expectations for AI-related growth.

Asia experienced similarly dramatic moves the following day. Taiwan’s TAIEX index dropped 9%, while South Korea’s Kospi tumbled more than 8% and triggered a temporary market-wide trading halt. Japan’s Nikkei fell as much as 6% and more than 20% from its latest high. China’s Star 50 and AI Index each declined as much as 5% before recovering some losses.

The regional damage reflected global links in the AI supply chain. Asian companies manufacture much of the hardware supporting U.S. AI ambitions. Their shares therefore reacted quickly to fears that American infrastructure spending could slow.

New political scrutiny could affect AI companies before projects are even built. The article says a newly Democrat-controlled Congress could tighten oversight of AI infrastructure projects. More reviews or restrictions could create delays, increase construction costs, and make some data centers harder to complete. That would matter because markets have increasingly tied stock gains and economic growth to AI investment.

The key mechanism is weaker expected profitability. If a company spends more money or waits longer before a data center begins operating, its expected returns fall. If a project is canceled, planned demand for chips, memory, equipment, and related services may disappear. Investors could then reduce the prices they are willing to pay for companies linked to the AI buildout.

The risk is not yet fully known. Analysts said volatility may continue until it becomes clearer how much construction is genuinely threatened. Some still view the sell-off as a correction, but delayed or canceled data centers remain a central concern for valuations.

Taiwan, South Korea, and Japan are especially exposed because their companies are connected to the global AI hardware cycle. The article identifies Taiwan as home to critical AI companies, including chip supplier TSMC. South Korea’s Samsung and SK Hynix supply memory used by AI data centers. Japan is also linked to the broader technology and hardware chain.

The mechanism is straightforward: slower U.S. construction can mean weaker demand for the equipment and components made across Asia. Investors therefore reacted strongly to fears that American AI infrastructure spending might lose momentum. Tai Hui of J.P. Morgan said the scale of the Asian correction followed the sharp U.S. decline.

Friday’s market moves showed this sensitivity. Taiwan’s TAIEX dropped 9%, South Korea’s Kospi fell more than 8%, and Japan’s Nikkei declined as much as 6%. The Nikkei had fallen more than 20% from its latest high, entering bear-market territory. Further U.S. uncertainty could keep regional volatility high.

China may continue investing in AI and semiconductors because the goals extend beyond near-term financial returns. The article says China treats AI and semiconductor self-sufficiency as strategic national priorities. That makes investment part of a broader effort to build technological independence, strengthen industrial competitiveness, and support national security.

This differs from the United States, where the article describes much AI investment as commercially driven. A company focused mainly on immediate profitability may delay construction when costs rise or demand becomes uncertain. China can have additional reasons to keep funding the sector, even if some projects do not quickly produce large profits.

Chinese AI-related markets still suffered during the sell-off. The Star 50 and AI Index each fell as much as 5% before recovering some losses. Charu Chanana said parts of China’s AI equity bubble may already have burst. However, strategic priorities could keep investment moving despite weaker market sentiment or uncertain short-term returns.

An AI data center is a specialized facility that houses powerful computers for developing and operating AI systems. It needs large clusters of processors, fast memory, networking equipment, electricity, cooling, buildings, and connections to communications infrastructure. The article focuses on these facilities because they sit at the center of the current AI investment cycle.

The mechanism is cumulative. A new data center requires chips to perform calculations and memory to store and move information. It also needs power systems, cooling equipment, construction work, and network links. That spending creates business for semiconductor makers and other suppliers. Companies such as Samsung, SK Hynix, and TSMC are linked to this hardware demand, according to the article.

If data-center construction slows, demand across that chain can weaken. The article says newly elected lawmakers could delay or cancel projects, raising costs and threatening expected profits. That possibility helps explain the sharp U.S. and Asian market declines, while future valuations depend on how much construction actually proceeds.