DeepSeek Fears Hit Samsung and SK Hynix Shares
South Korean shares fell as investors questioned memory-chip demand and the durability of AI spending. Samsung Electronics and SK Hynix came under pressure after Chinese AI lab DeepSeek said its V4.1-Flash model, released on September 10, uses a quarter of the high-bandwidth memory (HBM) required by earlier models during inference, as well as one-eighth of the SSD storage. DeepSeek cited compression and quantisation techniques behind the efficiency gains.
Around September 11, Samsung shares fell about 3.5% and SK Hynix dropped 2.2%. By mid-October, the KOSPI had fallen more than 7% intraday to a six-month low near 5,554, while the two chipmakers lost roughly 9–11% over the same period. Investors are also weighing competition from Chinese memory producer CXMT and concerns that tighter financial conditions and rising inflation could slow AI infrastructure spending.
The article says the stocks trade at about 2.6–3.1 times earnings, below global peers, and KB Securities called the declines an “asymmetric investment opportunity.” However, wider adoption of memory-efficient AI models could affect memory pricing, while stronger competition and slower AI investment pose further risks. DeepSeek’s claims have therefore intensified uncertainty around the outlook for HBM demand.
Neutral
The story is primarily about South Korean equities and memory-chip demand, so its direct effect on cryptocurrency markets is limited. It does, however, add uncertainty around AI infrastructure spending and technology valuations. In the short term, traders may treat the sharp declines in the KOSPI and chipmakers as a risk-off signal, potentially weighing on crypto assets if broader technology shares weaken and investors reduce exposure to volatile markets. The impact would likely be sentiment-driven rather than a change in crypto fundamentals.
A useful historical parallel is the market reaction to DeepSeek-related AI efficiency claims in early 2025, when concerns about reduced infrastructure needs contributed to volatility in technology shares and prompted traders to reassess AI-linked valuations. Similar headlines can also influence crypto tokens associated with AI, although price moves tend to depend more on overall risk appetite, liquidity, Bitcoin’s direction and token-specific news.
Over the longer term, more efficient AI models could reduce the amount of hardware required per workload, but lower costs may also expand AI usage and sustain demand for compute and memory. For crypto markets, the net effect is uncertain. Traders should monitor follow-through in Asian technology stocks, AI capital-spending guidance, funding conditions and crypto market flows rather than assume that the chip sell-off will translate directly into a lasting move in digital assets.