The rapid expansion of artificial intelligence is triggering a global shortage of a previously overlooked and inexpensive type of microchip—dynamic random-access memory, or DRAM—sending shockwaves through the tech industry. Once considered a commodity, DRAM is now in high demand as AI companies race to deploy powerful models that require massive amounts of memory to train and run. This surge in demand is putting pressure on supply chains and threatening to drive up prices across a wide range of consumer electronics. DRAM chips are essential for storing data temporarily while a device is in use, making them critical components in everything from smartphones and laptops to servers and gaming consoles. Traditionally, their production was stable and cost-effective, but the explosion of AI workloads has changed that. Large language models, image generators, and other AI systems require vast memory bandwidth and capacity, pushing data centers to deploy more DRAM-intensive hardware than ever before. Major AI firms, including OpenAI, Google, Meta, and Amazon, are investing heavily in custom AI chips and server infrastructure, all of which rely heavily on DRAM. This has led to a sharp spike in demand, outpacing supply. As a result, DRAM prices have begun to climb after years of decline, with industry analysts predicting sustained shortages through at least 2025. The ripple effects are already visible. Consumers may soon face higher prices for new laptops, gaming consoles, and even smartphones, as manufacturers pass on rising component costs. Meanwhile, data centers—key to powering AI services—are confronting tighter budgets and longer wait times for critical hardware. Some companies are delaying expansion plans or scaling back ambitious AI projects due to limited access to memory chips. The shortage is also prompting a shift in how the industry approaches memory. Companies are exploring new architectures, such as high-bandwidth memory (HBM), which offer faster performance but come at a much higher cost. While HBM is already being used in cutting-edge AI accelerators, its scarcity and expense make it impractical for widespread use. Experts warn that without significant investment in new DRAM manufacturing capacity, the bottleneck could slow the pace of AI innovation and make advanced technologies less accessible. The situation underscores a broader truth: the future of AI is not just about algorithms and compute—it’s also about the physical infrastructure that makes it possible. As demand for AI grows, so too does the need for a resilient and scalable supply of fundamental components like DRAM. Without action, the benefits of AI may be delayed, and consumers may end up paying the price.