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Seoul officials have identified memory shortage as the primary bottleneck in AI development, warning of supply-demand imbalance and geopolitical tensions. Capacity additions are not expected until 2027, raising concerns for AI progress and global competitiveness.
Seoul officials have officially recognized memory shortages as the critical bottleneck in AI development. During a recent briefing, the chairman of SK Group, Chey Tae-won, highlighted the growing demand for AI memory and the lack of new capacity coming online, which could hinder AI progress and impact global supply chains.
Chey Tae-won, speaking at the Korea Chamber of Commerce and Industry’s Jeju Forum, stated that customer demand for AI memory is expected to increase by 60 to 100 percent in 2027 compared to 2026. With AI now accounting for over half of semiconductor consumption, he estimated overall demand growth at a minimum of 50 to 60 percent.
He emphasized that no significant new capacity is expected to be operational in 2027, describing the situation as a near-chaotic lobbying environment driven by both corporate and government actors. Chey warned that this imbalance could lead to geopolitical tensions, as foreign governments begin to treat memory access as a matter of national security.
SK hynix’s recent investments, including a planned capacity increase at the Yongin mega-cluster and a shift to dedicated HBM production, aim to address part of this gap. However, none of these capacity expansions will be completed before 2027, creating a looming supply shortfall.
Implications of Memory Shortage for Global AI Progress
This recognition by Seoul underscores a critical vulnerability in AI development: the supply of high-bandwidth memory (HBM) and related components is insufficient to meet surging demand. The shortage could slow AI innovation, increase costs, and provoke geopolitical conflicts as countries vie for control over key semiconductor resources.
Furthermore, the warning signals a potential shift in global supply chains, with memory becoming a strategic asset intertwined with economic security policies. Companies and nations that secure early access to memory capacity may gain a competitive edge, while those facing shortages could face delays and increased costs.
High Bandwidth Memory (HBM) modules
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Memory as a Strategic Bottleneck in AI Ecosystem
Recent industry reports, including Counterpoint Research, indicate that SK hynix held 58 percent of global HBM revenue in Q1 2026, with Samsung and Micron sharing the remaining market. Despite high demand, capacity additions have been delayed, with SK hynix announcing plans for capacity expansion only in 2027. The industry has been experiencing sustained demand growth, with projections of a 33 percent compound annual growth rate for HBM through 2030.
Historically, memory supply has been a limiting factor in AI advancements, but the current situation is exacerbated by geopolitical considerations and concentration in a few key suppliers. The industry’s physics—where high-bandwidth memory is bonded to AI accelerators—means that shortages directly impact AI training and inference capabilities.
“No company has meaningful new capacity coming online next year.”
— Chey Tae-won, SK Group Chairman
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Unresolved Questions on Capacity and Geopolitical Impact
It remains unclear whether SK hynix’s planned capacity expansions will fully meet the surging demand or if other manufacturers will accelerate their investments. Additionally, the precise geopolitical responses to potential shortages are still developing, with some governments already starting to treat memory access as a matter of national security.
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Next Steps for Industry and Policy Responses
Industry players are expected to accelerate capacity investments, possibly through new fabs or technology innovations. Governments may also intervene more directly, potentially restricting memory exports or supporting domestic manufacturing. Monitoring these developments will be crucial in assessing how the supply-demand imbalance evolves and how AI progress is affected.
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Key Questions
Why is memory considered the bottleneck in AI development?
Memory, especially high-bandwidth memory (HBM), is essential for AI training and inference. The current supply cannot meet the rapidly increasing demand, creating a bottleneck that can slow AI progress and increase costs.
What are the main causes of the memory shortage?
The shortage is driven by surging demand from AI applications, limited capacity additions expected before 2027, and market concentration among a few key suppliers like SK hynix, Samsung, and Micron.
How might geopolitical tensions influence memory supply?
Governments are increasingly viewing memory access as a matter of economic security, which could lead to export restrictions, trade disputes, or increased investment in domestic manufacturing, further affecting global supply chains.
Will capacity expansions solve the shortage?
While SK hynix and others plan capacity increases by 2027, these will not address the immediate shortfall in 2026. The supply-demand gap is expected to persist into the near future unless new investments or technological breakthroughs occur.
What does this mean for AI developers and companies?
AI development may face delays or increased costs due to hardware shortages. Companies with existing memory hardware might have a strategic advantage, as owning hardware can mitigate supply risks.
Source: ThorstenMeyerAI.com
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