A new ai catastrophe: sell-off from wall street to seoul

NQ100

Key zone: 29,500 -30,000

Buy: 30,500 (on strong positive fundamentals); target 31,500-32,500; StopLoss 30,000

Sell: 29,000 (on a decisive break above 29,500); target 27,500-26,500; StopLoss 29,500

Inflationary pressure and high borrowing costs are forcing investors to reassess the prospects for AI. The market has exposed the main problem facing technology stocks: building AI infrastructure requires substantial capital investment, while the returns on that investment are highly sensitive to financing conditions.

Even strong financial results from semiconductor manufacturers no longer guarantee stability in their share prices. The market is focused not only on demand for computing capacity but also on the ability of the entire AI business ecosystem to convert growing revenue into sufficient cash flow to cover expenses and service financial obligations.

A Quick Recap:

On October 15, technology markets worldwide came under pressure following the release of U.S. inflation data. September's Consumer Price Index (CPI) rose to 3.7% year over year, compared with 3.4% a month earlier. This heightened concerns that the Federal Reserve would have to maintain restrictive monetary conditions for longer or raise interest rates again.

The previous day, the Nasdaq Composite fell 1.9%, while the S&P 500 declined 1.1%. On the morning of October 15, Nasdaq 100 futures lost 1.1%, falling to 29,842.75 points, while S&P 500 futures dropped 0.4% to 7,740.25 points. The sharpest reaction occurred in Asia: South Korea's Kospi plunged approximately 7.6%, Japan's Nikkei 225 fell 4.9%, and Taiwan's Taiex declined 3.4%.

The depth of the decline reflects the high concentration of capital in companies directly involved in developing AI infrastructure. Samsung Electronics shares fell approximately 9.5%, SK Hynix lost 11.1%, and SoftBank declined 9.3%. This synchronized sell-off demonstrates how closely market valuations of memory manufacturers, technology holding companies, and other participants in the AI ecosystem are tied to expectations of continued industry growth.

AI's Key Vulnerability Is the Gap Between Today's Massive Spending and the Timeline for Future Returns.

Building data centers requires substantial upfront expenditures on computing accelerators, server equipment, power infrastructure, and cooling systems. Meanwhile, recovering invested capital may take several years, and technological equipment risks losing its competitiveness even before it has been fully depreciated.

In a high-interest-rate environment, this model becomes less attractive. If expected cash inflows fail to offset rising costs, companies are forced to revise their investment plans, while investors lower their valuations of future earnings.

• The greatest risks arise for highly leveraged companies. Large cloud platforms with stable operating cash flows are in a stronger position, although even they are not immune to declining returns on substantial capital expenditures.
• For chipmakers, what matters is not only current orders but also the willingness of cloud companies to continue expanding computing capacity. If major customers begin cutting budgets or postponing project launches, the consequences could quickly spread to manufacturers of accelerators, memory chips, and server equipment.
• The development of China's semiconductor industry is another source of pressure. Expanding supply from companies such as CXMT increases the risk of stronger price competition in the memory market. Over time, this could constrain manufacturers' profit margins even if substantial demand for AI equipment persists.

Monetary policy remains one of the key risk factors. On October 15, the yield on 10-year U.S. Treasury bonds fell to approximately 5.10%, while the two-year yield declined to 4.72%. However, declining yields do not necessarily indicate improving financing conditions: they may reflect safe-haven demand for government securities amid a sell-off in risk assets.

The inflation threat has not disappeared either. High oil prices can sustain price pressures, while data centers' growing electricity requirements increase the industry's operating costs. Consequently, the Federal Reserve has less room to ease monetary policy quickly, and the cost of capital may remain a constraint on the AI investment cycle for longer.

So What Is the Bottom Line?

The current sell-off does not prove that demand for artificial intelligence has peaked. It points to something else: investors are beginning to distinguish AI's long-term technological potential from the financial resilience of the companies funding its development. If expected returns fail to justify the amount of invested capital, even rapidly growing industry participants will face downward revisions to their market valuations.

• Stock market: Further compression of technology company valuation multiples could intensify pressure on broader indices, particularly if the correction affects the largest stocks with significant weights in index baskets.
• Credit market: Highly leveraged companies risk facing higher costs for new borrowing and refinancing. Data center operators that must continue capital expenditures despite limited free cash flow are particularly vulnerable.
• Semiconductor sector: Revisions to cloud platforms' budgets could slow order growth. Stronger price competition in the memory market may create additional pressure.
• Global economy: A reduction in AI investment would affect not only chip manufacturers but also suppliers of servers, power equipment, and industrial infrastructure, limiting demand associated with the technology boom.

Over the coming weeks, the base-case scenario remains elevated volatility, with the risk of further declines in technology stocks. If credit risk continues to rise, the stock market could receive another bearish signal.

So we act wisely and avoid unnecessary risks.

Profits to y’all!