Dan Ives Keeps $350 IBM Target After 26% Weekly Crash

Citi expects shortages across the global memory semiconductor market to deepen through 2031, driven by rising demand for high-bandwidth memory, DRAM and NAND as artificial intelligence systems adopt continual learning. In a research note published Monday, the investment bank identified continual learning as a potential central theme in AI development over the next five years, an approach that updates AI models with new tasks while retaining prior knowledge and so drives demand for both memory and data storage. Citi forecasts HBM bit demand will increase 62% year over year to 75.2 billion gigabits in 2027, followed by a further 69% rise to 127.0 billion gigabits in 2028, and it expects demand for enterprise solid-state drives to expand as AI systems require additional storage capacity. For DRAM, Citi projects global demand growth of 30% in 2027 and 35% in 2028, against supply growth of 19% and 22%, producing supply-demand ratios of -8.7% and -9.7% for the respective years. The bank anticipates a similar imbalance in NAND memory, forecasting demand growth of 29% in 2027 and 33% in 2028 versus supply increases of 21% and 25%, with NAND supply-demand ratios of -6.1% in 2027 and -5.5% in 2028 compared with an estimated -0.8% in 2026. In its equity research, Citi named Samsung Electronics, SK Hynix, Micron, Sandisk and Kioxia as its preferred memory semiconductor stocks, and highlighted Montage, Applied Materials, Lam Research, TES, Eugene Tech and TechWing within semiconductor equipment and materials.

UBS now expects artificial intelligence capital expenditure to reach nearly $1 trillion in 2026 before climbing to around $1.4 trillion in 2027, with surging memory costs accounting for most of the increase. The bank's updated estimates put total AI capex at $998 billion this year, almost double the $506 billion recorded in 2025, and forecast spending of $1.447 trillion next year. Memory is emerging as the main source of that growth, with UBS estimating memory spending will climb from $71 billion in 2025 to $367 billion this year and $923 billion in 2027, while other AI-related costs are estimated at $631 billion in 2026 before declining to $525 billion next year. That means higher memory costs will account for about 60% of the increase in AI capex this year and more than the entire net increase in 2027, and across the two years UBS calculates that roughly 90% of the nearly $1 trillion increase in AI capital expenditure between 2025 and 2027 will come from higher memory spending. Memory represented about 14% of total AI capex in 2025, a share the bank estimates will rise to 37% this year and reach 64% in 2027, and UBS said price-driven increases would add relatively little to real U.S. gross domestic product, instead representing a transfer of income and profits toward memory producers in Asia.

Micron Technology and Intel CEOs cautioned that memory chip shortages and higher prices could persist through 2027, with Micron's leadership indicating on a recent call that supply constraints may only start to ease meaningfully from 2028 onward. Intel's CEO echoed the outlook for extended tightness in DRAM and NAND availability, pointing to heavy AI and data center demand. Micron Technology designs and produces memory and storage hardware used in everything from smartphones and PCs to data centers, so long running tightness in DRAM and NAND supply directly touches the products it sells into these markets. As one of the larger US based chip manufacturers by scale, with a reported market value of about $1.1 trillion, its comments on supply conditions can influence how investors think about capacity planning across the wider semiconductor sector. The clearest test of this read will be how Micron's long term customer agreements and utilization plans look through 2027, especially whether the company keeps reporting high take or pay coverage across its AI oriented memory output as new fabs and its 512GB DDR5 modules move toward volume production in the second half of 2027.