Morgan Stanley: Memory Availability Now Primary Constraint on AI Growth
Analyst Joseph Moore views recent stock corrections as a healthy adjustment, signaling a strategic re-entry point as memory becomes the AI supply chain's main bottleneck.
Morgan Stanley has signaled that the recent correction in memory semiconductor stocks has concluded, creating a strategic re-entry point for investors. Analyst Joseph Moore argues that the downturn reflects a necessary market adjustment rather than a deterioration of industry fundamentals.
According to Moore, the recent decline in stock prices was a "healthy pricing in of durability concerns" rather than a fundamental collapse of the sector. He posits that the current market environment is characterized by a critical supply-demand imbalance, stating that memory availability now represents "increasingly THE primary constraint on AI demand." While Moore acknowledges that this specific supply constraint will not last forever, he believes it will persist long enough to drive stock prices materially higher.
The Cycle of AI Infrastructure
The memory semiconductor market has faced significant volatility following a massive rally fueled by the build-out of AI infrastructure. This volatility has manifested as periodic corrections, with analysts from Morgan Stanley and Mizuho observing drawdowns ranging from 14% to 21%. Since mid-2025, these dips have emerged as a recurring pattern in the sector's growth cycle. Rather than signaling a market peak, these corrections are viewed as standard volatility within a broader upward trajectory driven by the scaling of large-scale AI models.
Shifting Bottlenecks in the Supply Chain
This outlook underscores a pivotal shift in the AI hardware landscape. For much of the initial AI boom, the primary bottleneck was pure compute power, dominated by GPU availability. However, as models grow in complexity, the constraint has shifted toward memory capacity and bandwidth, specifically High Bandwidth Memory (HBM).
Because memory has become the limiting factor for AI expansion, the companies providing this critical hardware—including Micron, Samsung, and SK Hynix—possess significant strategic importance and pricing power. This shift transforms memory from a commodity component into a primary gatekeeper for the global AI supply chain, insulating the sector from typical cyclical downturns.
Future Outlook and Market Risks
Investors are currently weighing efficiency initiatives that could potentially reduce the demand for raw memory capacity. One such example is Google's "TurboQuant" memory efficiency initiative. However, Moore describes TurboQuant as an "evolutionary development" that brings "basically no surprises for memory," suggesting that software-level optimizations are unlikely to offset the massive hardware requirements of next-generation AI.
Market participants will now be watching for signs of capacity expansion from major chipmakers to see if supply can eventually meet the AI-driven demand. For now, the consensus from Morgan Stanley suggests that the fundamental need for memory remains the dominant force driving the sector's valuation.