⚡ KEY INSIGHT
AI model training and deployment will face persistent hardware bottlenecks as memory supply fails to meet explosive demand.
Memory manufacturers including Samsung, SK Hynix, and Micron are ramping production but are projected to supply only 60% of global DRAM demand through 2027, with some industry leaders warning shortages could extend to 2030. This supply-demand gap directly impacts AI infrastructure, as training large language models and scaling inference workloads require enormous amounts of high-bandwidth memory. The extended shortage will likely increase hardware costs and slow deployment timelines for data centers racing to build out AI capacity.