Memory constraints, not computing power, are emerging as the primary bottleneck in artificial intelligence development, according to Micron Technology Chief Executive Sanjay Mehrotra, who warned that supply limitations could extend well beyond 2026.
Memory supply pressures intensify
Mehrotra said sustained shortages in memory manufacturing stem from structural challenges, including long construction timelines for wafer plants and diminishing efficiency gains in advanced production nodes. Building a new facility typically takes three to four years, with additional time required to reach full output.
He emphasized that every leap in computing capability must be matched by proportional memory expansion. The rapid growth of larger AI models, longer context windows, and increased token processing continues to drive demand for high-performance memory at a pace that supply struggles to match.
Structural imbalance reshapes semiconductor industry
The imbalance is not a short-term cycle but a deeper industry shift. Gains in bit output per wafer are slowing with each new technology generation, tightening supply even as demand accelerates.
This shift is increasingly tied to the rise of high-bandwidth memory (HBM), where leading producers such as Samsung, SK Hynix, and Micron have already allocated their full production capacity through 2026. As manufacturing pivots toward higher-margin AI-focused memory, the availability of traditional DRAM and NAND declines, effectively reducing total usable supply.
Analysts project that global HBM demand for AI servers will grow 35-fold between 2024 and 2028, reflecting the scale of infrastructure required to support next-generation AI systems.
Prices surge as demand outpaces supply
The supply-demand imbalance is driving sharp price increases across memory markets. Earlier forecasts pointed to gains of 40% to 50% in early 2026, with more recent estimates showing DDR5 prices rising another 30% to 50% through midyear.
At the same time, the overall memory market is expected to expand rapidly. TrendForce projects total revenue could reach $552 billion in 2026, representing a 134% increase driven largely by AI infrastructure demand.
Data centers dominate resource allocation
The shift toward AI is transforming how memory resources are distributed. By 2026, data centers are expected to consume up to 70% of high-end memory output, a reversal from previous years when consumer devices such as smartphones and laptops dominated demand.
This redistribution creates a zero-sum dynamic, where increased allocation to AI systems directly reduces supply available for other sectors, including automotive and consumer electronics.
Market ripple effects extend beyond semiconductors
The impact of these constraints is spreading into broader financial markets. A semiconductor sector sell-off on June 5 erased roughly $1.3 trillion in market value, followed by a $130 billion decline in digital asset markets.
The correlation highlights how closely sentiment around AI infrastructure is tied to other high-risk, technology-driven assets. When confidence in the semiconductor outlook weakens, capital flows quickly shift across adjacent sectors, including AI-linked digital tokens with a combined market value exceeding $21.5 billion.
Memory emerges as backbone of AI era
Mehrotra said Micron’s $200 billion investment program in U.S. manufacturing is designed to address these long-term constraints through phased, data-driven expansion. He stressed that aligning production capacity with technological progress is critical to sustaining growth in a cyclical industry.
Ultimately, he described memory as the foundation of the AI era, underpinning both performance and scalability as computing expands from centralized data centers to edge environments. With demand for storage-intensive applications accelerating, memory is set to remain a defining factor in the evolution of artificial intelligence over the coming decade.
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