Morgan Stanley warns AI’s biggest bottleneck is now memory, not compute

The AI industry’s next bottleneck is not about processing speed — it is about memory. Morgan Stanley published a sweeping report this week arguing that the central constraint in artificial intelligence is migrating from a “compute wall” to a “memory wall,” a shift the firm says will reshape the entire AI infrastructure investment landscape over the next half-decade.

The Widening Gap

The bank’s analysis, led by Shawn Kim, Head of Morgan Stanley’s Europe and Asia Technology Team, lays out the mismatch in stark terms. DDR5 single-channel bandwidth is expected to grow from 44.8 GB/s in 2024 to just 51.2 GB/s in 2026 — roughly a 14% increase over two years. In that same window, global AI inference token generation is projected to leap from approximately 10 trillion tokens per month to 3,200 trillion, a more than 320-fold surge. The result is a rapidly widening chasm between what processors can handle and what memory systems can deliver. Mmoomoo

That imbalance is already showing up in costs. Storage-related components now account for as much as 73% of CPU server bill-of-materials expenses, and DRAM prices per gigabyte have climbed to their highest levels in nearly 30 years. According to TrendForce, conventional DRAM contract prices surged by more than 90% quarter-over-quarter in the first quarter of 2026, while Gartner estimates annual DRAM prices this year will rise by 125%. Ssokatec Rreddit BBiggo Mmoomoo

A New Investment Frontier

Morgan Stanley forecasts that cloud storage spending will reach $418 billion by 2030, with memory’s share of cloud providers’ capital expenditures rising from 12% in 2023 to 40% by 2027. The total addressable market for novel memory technologies, including high-bandwidth memory (HBM), could reach $276 billion by 2030. Mmacrostream Mmoomoo

The firm identified six areas of innovation it expects to drive the next wave of AI infrastructure spending: advanced process nodes, memory architecture redesign, advanced packaging, peripheral interconnect chips such as CXL, processing-in-memory, and new materials. In a June podcast, Kim noted that memory prices have risen more than six-fold over the past year, describing the situation as “chipflation” — when memory chips stop getting cheaper and become harder to find. Mmoomoo Mmorganstanley

Ripple Effects Beyond Data Centers

The supply crunch is already spilling into consumer electronics. Morgan Stanley projects that PC memory demand could face a 15% shortfall in 2027, equivalent to about 58 million units, while smartphones could see a 12% shortfall affecting roughly 134 million devices. Reuters reported in June that makers of devices from smartphones to PCs are being forced to choose between raising prices and accepting thinner margins. Rreuters Mmorganstanley

The firm’s preferred investment plays include Micron, Samsung, SK Hynix, and SanDisk, alongside semiconductor equipment makers such as ASML. As Kim put it: “GPUs determine how fast AI runs, while memory determines how far AI can go.” YYahoo Finance Ttradingview Mmoomoo