- Intel ($INTC) CEO Lip-Bu Tan said AI-driven memory shortages could become even more severe in 2027, with some memory prices already rising 5x to 7x. - He said memory can account for as much as 70% to 80% of the BOM in some budget smartphones and laptops, meaning sustained price increases could pressure low-margin consumer device production first. - The article attributes part of the broader shortage to HBM expansion competing with commodity DRAM for manufacturing capacity, tightening supply beyond HBM itself. - $INTC views advanced packaging that integrates CPUs, AI accelerators, HBM, and high-speed I/O into increasingly large packages as a key strategy, highlighting EMIB as an alternative approach to CoWoS. - Tan also identified package substrates as a bottleneck, saying the AI substrate supply chain is heavily concentrated among two major suppliers in Japan and two in Taiwan. - He argued that reinforcement learning and AI agents are reviving CPU demand because CPUs increasingly handle orchestration across models and applications, while Intel is currently unable to satisfy all requested CPU volume. - Intel is pursuing a "co-opetition" strategy with $NVDA, combining Intel CPUs with Nvidia GPUs and NVLink while continuing to compete in other areas. - Tan also highlighted power and cooling as additional AI infrastructure constraints, with Intel investing in approaches including liquid cooling and microfluidic cooling. > The bigger message is that AI bottlenecks are spreading from memory → advanced packaging → substrates → CPUs → power → cooling. Looking at AI infrastructure only through GPUs increasingly misses large parts of the hardware stack. > On memory, the important point is the interaction between HBM and commodity DRAM capacity. If AI data-center demand remains elevated, the resulting capacity allocation could keep not only HBM but broader DRAM supply and pricing tight. > Packaging may be the most interesting part. As accelerators become larger and integrate more HBM, EMIB/CoWoS-class integration, substrates, thermal management, and testing all become more critical. AI scaling is increasingly shifting from simply designing a better GPU to being able to manufacture and support an increasingly complex system.