DRAM prices will rise between 50% and 55% this quarter versus the fourth quarter of 2025 — an increase TrendForce analyst Tom Hsu called "unprecedented." The culprit is not a supply shock or a trade war. It is high-bandwidth memory, the specialized chips stacked atop AI accelerators, and data centers now consume an estimated 70% of all memory chips produced worldwide.
HBM is sold out through 2026, with allocations for 2027 already being negotiated. The shortage has flipped the power structure of the AI hardware stack. Nvidia still designs the processors everyone wants. But the real power in AI infrastructure is shifting to the memory makers: Samsung, SK Hynix, and Micron.
A single Nvidia B300 GPU requires eight HBM chips, each containing 12 individual DRAM dies — 96 dies per GPU, or 768 dies for a fully configured DGX B300 system with eight GPUs. When Micron can only meet two-thirds of the medium-term memory requirements for some customers, according to its own executives, the constraint is no longer theoretical.
Who Wins When Memory Becomes the Moat?
HBM commands significantly higher margins than standard DRAM modules used in consumer devices, and Samsung, SK Hynix, and Micron have all been aggressively converting production lines to HBM, as the revenue per wafer for HBM is estimated to be three to five times higher than conventional DDR5. The economics are brutal for everyone else. A single silicon wafer provides 3x as much commodity DRAM as HBM, and fab processing time for HBM is significantly longer too, making the supply problem worse — producing more HBM equates to fewer total memory chips produced.
The three major memory manufacturers are enjoying significantly higher margins as DRAM prices surge, and SK Hynix, which has been the leading supplier of HBM chips, has seen its revenue from AI-related memory products more than triple since 2024.
In October, SK Hynix said it had secured demand for its entire 2026 RAM production capacity. Samsung and Micron are racing to catch up, but Samsung Electronics has struggled to meet Nvidia's qualification standards for its 12-layer HBM3E chips due to yield and performance issues, relegating the world's largest memory maker to a tertiary position.
The ripple effects extend far beyond AI labs. Your next laptop, smartphone, or even refrigerator is going to cost more — and you can thank AI for that, as the AI boom has triggered what insiders are calling "RAMageddon."
Memory now accounts for about 20% of the hardware costs of a laptop, up from between 10% and 18% in the first half of 2025.
Tesla CEO Elon Musk stated in late January 2026 that the company faces a "chip wall," describing the constraint as forcing a stark choice to "hit the chip wall or make a fab."
Can $750 Billion Buy Enough Compute?
The money flooding into AI infrastructure has reached a scale that defies easy comparison. The capital expenditure of the 14 largest publicly owned data center operators globally is seen close to $750 billion this year against a little less than $450 billion last year.
JPMorgan raised its estimate for global AI-related capital expenditures through 2030 to $5.5 trillion, up from $5.1 trillion, driven by greater capacity expansion and increased debt financing.
Goldman Sachs Research's baseline model implies $765 billion in annual AI CapEx in 2026, growing to $1.6 trillion in annual CapEx in 2031.
At Nvidia's GTC conference in March, CEO Jensen Huang said the company doubled its demand forecast within the next year: "I see through 2027 at least $1 trillion. In fact, we are going to be short. I am certain computing demand will be much higher than that."
The capital is not evenly distributed. Microsoft expects to invest roughly $190 billion in capital expenditures in calendar year 2026, a 61% increase from the previous year.
The four largest hyperscalers — Amazon, Google, Microsoft, Meta — are expected to spend more than $350 billion on capex in 2025, and including other tech players pushes the total toward an estimated $0.5 trillion in 2025.
But money alone does not solve the bottleneck. Power availability — not capital — is the primary constraint on data center development, and electrical grid interconnections are often taking up to four years.
The Department of Energy already projects data centers will account for up to 12% of U.S. electrical demand by 2028, and the grid is not ready.
Chevron and Microsoft signed a 20-year agreement to supply dedicated power for a planned data center campus near Pecos, Texas, one of the largest pairings of compute infrastructure and on-site generation in the US.
