Samsung has reportedly locked up 70% of its global HBM capacity through 2031 via long-term contracts with major AI chipmakers.

Microsoft, Nvidia, and Google are quietly securing multiyear supply agreements that extend nearly a decade into the future. These contracts reveal exactly where the AI memory market is heading.

Key takeaways: Samsung has locked approximately 70% of its high-bandwidth memory (HBM) capacity through long-term agreements extending to 2031. Buyers are securing modules at $300 to $400 each under these contracts, compared to roughly $2,100 on the spot market. SK hynix’s CEO has cautioned that the shortage may persist through 2030, despite the company committing $38.3 billion to HBM expansion through 2031. Economists point to Jevons’ paradox: as AI computing becomes cheaper and more efficient, total memory demand increases rather than decreases, because lower costs enable broader economic deployment. Act now: the analyst who called NVIDIA in 2010 just named his top 10 AI stocks — and Micron Technology didn't make the cut. Grab the names FREE today.

The AI boom is transforming memory from a cyclical semiconductor component into critical infrastructure. While manufacturers can break ground on new fabrication plants, qualifying, ramping, and scaling advanced HBM takes years. Consequently, AI developers are securing supply commitments years in advance.

The latest data originates in South Korea, where Samsung Electronics has reportedly committed approximately 70% of its memory production capacity through long-term agreements (LTAs) extending to 2031. Rather than signaling that the shortage is easing, this move indicates that buyers anticipate prolonged scarcity. According to the Seoul Economic Daily, Samsung has secured these LTAs with major clients including Microsoft (NASDAQ: MSFT), Nvidia (NASDAQ: NVDA), and Google.

Enterprises are accepting multiyear terms to secure two advantages simultaneously: guaranteed allocation and significant cost savings. A 36GB HBM3E module trades at approximately 2.87 million won ($2,100) on the spot market, whereas LTA buyers pay only 500,000 to 700,000 won ($300 to $400). Companies are willing to lock in purchases years ahead to obtain memory at a fraction of current spot prices and shield themselves from market exclusion as AI demand accelerates.

These arrangements offer Samsung predictable revenue streams while providing customers with lower costs and supply certainty. The underlying reality is straightforward: in an AI-driven era where memory shortages can bottleneck entire data centers, physical inventory outweighs future purchasing options. Industry tracker TrendForce noted that negotiations for 2027 HBM supply remain stalled, as manufacturers advocate for higher pricing while AI-driven demand continues to displace conventional DRAM production.

Big Tech is quietly acquiring the next decade of AI infrastructure, effectively leaving competitors to compete for remaining capacity. © 24/7 Wall St. Increased production capacity does not automatically translate to immediate market availability. Samsung and its rivals are actively investing in expansion. Samsung is reportedly evaluating the conversion of its S5 foundry line at its Pyeongtaek campus specifically for memory manufacturing.

SK hynix (NASDAQ: SKHY) is advancing an even larger capital commitment. The company recently broke ground on a $4 billion HBM packaging facility in Indiana, targeting production commencement in 2029. Its board has additionally authorized 54.3 trillion won (approximately $38.3 billion) in investments through 2031. SK hynix’s CEO has explicitly stated that the memory shortage could endure through 2030. Micron Technology (NASDAQ: MU) is positioned to capitalize on identical market dynamics. Although all three primary suppliers are racing to scale HBM output, enterprise customers are concurrently reserving future allocations. New fabrication plants will eventually increase supply, but pre-committed capacity ensures that much of that output will not reach the open market.

Investors should note a critical economic dynamic: LTA pricing sits dramatically below current spot rates, which could reduce per-unit HBM expenditures as AI computing scales. However, lower costs do not equate to reduced aggregate demand. This phenomenon aligns with Jevons’ paradox, first articulated by British economist William Stanley Jevons in 1865. Jevons observed that as steam engines became more fuel-efficient, coal consumption did not decline; instead, cheaper energy prompted factories to purchase more engines, driving total usage upward. Applied to AI, reduced inference costs will likely incentivize organizations to deploy additional models, onboard more users, and automate expanded workflows. Efficiency ultimately enlarges the addressable market. Consequently, investors should not assume that current memory intensity will diminish as AI silicon improves.

Current metrics indicate a structural memory shortage extending well beyond the near term. Samsung’s commitment of 70% of its capacity through 2031 is particularly telling, as enterprises are effectively prepaying for supply certainty years before new fabrication lines become operational. This dynamic should sustain revenue visibility for Samsung, SK hynix, and Micron amid structurally elevated HBM demand. The primary risks involve potential deceleration in AI capital expenditure or customer overordering. Nevertheless, prevailing indicators contradict those scenarios. For investors, the more accurate framework is not that memory will stay perpetually scarce, but rather that the sector has entered a multiyear phase where supply expansion struggles to match the volume of AI computing that declining costs render economically feasible.

For questions or corrections, please contact the editorial team. Rich Duprey spent two decades patrolling suburban neighborhoods as a police officer before transitioning to full-time financial journalism. Over the past 20 years, he has focused on identifying durable long-term equity holdings while covering consumer goods, technology, and industrial sectors. Drawing from a non-traditional analytical background, Duprey specializes in translating complex market dynamics into actionable insights for retail investors. His work has been published across The Motley Fool, InvestorPlace, Yahoo! Finance, Money Morning, MarketWatch, Financial Times, Forbes, Fast Company, and USA Today.