SK Hynix Chairman Vows to Boost AI Memory Chip Production Amid Surging Demand

Stock News
02/23

The chairman of SK Group, Chey Tae-won, has pledged to expand production capacity for AI memory chips in response to a sharp increase in demand driven by global data center construction. Speaking at a conference in Washington on February 20, the head of South Korea’s second-largest conglomerate referred to high-bandwidth memory (HBM) as a "monster chip" that is generating massive profits for SK Hynix. Buoyed by record earnings, the chipmaker’s stock price has surged more than threefold over the past year. Although Chey did not specify the scale of the expansion, SK Hynix indicated in January that capital expenditure in 2026 will rise significantly compared to last year to meet demand for HBM chips. These chips are essential for producing accelerators designed by companies such as NVIDIA, which are used to train and run artificial intelligence services. U.S. tech firms, from Microsoft to Meta Platforms, plan to allocate approximately $650 billion this year toward infrastructure development in a bid to gain an edge in the AI technology race. This record spending is contributing to a global shortage of memory chips, a market dominated by SK Hynix, its domestic rival Samsung Electronics, and U.S.-based Micron Technology. SK Hynix has already sold out its entire memory chip production capacity for 2026, and Micron’s HBM supply is also fully booked. However, Chey also cautioned that rapid technological changes could alter the competitive landscape, potentially leading to losses in the future. According to Chey, analysts’ average forecast for SK Hynix’s 2026 annual operating profit rose from around $50 billion late last year to $70 billion in January, with some analysts further revising it upward to over $100 billion. "That certainly sounds like good news," Chey remarked, "but it could just as easily turn into a $100 billion loss." He also highlighted growing infrastructure challenges, noting that SK Group is exploring building power plants next to AI data centers, as failing to meet energy demands could be "disastrous."

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