Memory Prices May Surge 268%! AI Boom Propels Semiconductor Market Toward $1.7 Trillion

The semiconductor industry is entering a super growth cycle driven by AI infrastructure development. According to the latest forecast from TechInsights' McClean Report, the global semiconductor market could surge to approximately $1.7 trillion by 2026, a 98.3% year-on-year increase, and may surpass $2.2 trillion by 2027. Unlike previous cycles driven by consumer electronics such as PCs and smartphones, the rapid expansion of AI data centers is reshaping the entire supply chain, including GPUs, high-bandwidth memory (HBM), DRAM, NAND, advanced process nodes, and advanced packaging.

The memory market remains the most prominent. The McClean Report estimates that the global memory market will grow by 298% in 2026, reaching $916.4 billion, becoming the core engine driving the overall semiconductor market.

Notably, this remarkable market growth is not primarily driven by increased shipment volumes, but by rising prices. The report forecasts that memory bit shipments will increase by only 8% in 2026, while average selling prices could surge by 268%. In other words, the industry is not experiencing a simple 'sell more chips' boom, but rather a rapid price escalation of scarce memory products due to the swift expansion of AI demand.

HBM has become a critical bottleneck in the AI server supply chain. AI accelerators require extremely high memory bandwidth to continuously feed massive data to GPUs during training and inference of large models, making HBM an indispensable core component of AI computing systems.

However, the surge in HBM demand is also squeezing traditional DRAM supply. The McClean Report points out that, for the same bit capacity, HBM requires approximately three times the wafer area and about four times the manufacturing cost of standard DDR5. As major memory manufacturers such as Samsung Electronics, SK hynix, and Micron Technology shift more capacity toward HBM, the capacity available for traditional DRAM is being compressed.

This also means that the pricing pressure from the AI boom is not limited to HBM. Non-HBM DRAM used in PCs, servers, smartphones, automotive, and industrial electronics may also face supply constraints, further driving up overall memory costs.

TechInsights recently noted that AI-driven memory demand is creating one of the largest memory shortages in history, with DRAM and NAND prices likely to remain high for the coming years, indicating that this memory cycle has clear structural characteristics rather than being a short-term rebound from a traditional inventory cycle.

In addition to memory, the logic chip market is also being driven by AI infrastructure investment. The McClean Report forecasts that the MOS logic market will grow by 32% in 2026, reaching $577.5 billion, with GPUs being the primary growth driver. GPU revenue is estimated to increase by 31% in 2026, driven by both higher shipment volumes and increased average selling prices.

These GPUs have become core computing components in AI data centers. From training and inference of Transformer models to simulation computing, robotics, autonomous driving, and emerging physical AI applications in healthcare, massive parallel computing power is required. As AI model sizes continue to grow, demand for GPUs, HBM, and high-speed networking components in data centers is rising in tandem.

Within the GPU supply chain, NVIDIA (NVDA-US) is one of the most representative beneficiaries, and the continued expansion of AI servers is making advanced process nodes and advanced packaging capacity new supply bottlenecks. This means that competition in AI semiconductors is no longer just about single-chip performance, but extends to wafers, memory, packaging, and overall supply chain coordination capabilities.

Meanwhile, the microprocessor and microcontroller markets are also expected to gradually recover. The McClean Report believes that operating system upgrades, improved industrial demand, and stabilization in the automotive market will provide additional growth momentum for related products.

TechInsights' June analysis further indicates that the global semiconductor market is expected to surpass $2 trillion by 2027. Accelerating AI infrastructure investment, data center expansion, advanced packaging, memory demand, and computing demand are making this semiconductor growth cycle distinctly different from previous ones.

However, the rapid expansion of the market size also comes with a significant risk: whether suppliers will over-expand capacity during peak demand.

The semiconductor industry has experienced similar situations multiple times in the past. When demand rises rapidly, chip manufacturers often significantly increase capital expenditures, build new fabs, and expand capacity. However, when new capacity comes online simultaneously, increased supply may lead to inventory accumulation, price declines, and deteriorating profitability, ultimately triggering a market downturn.

Therefore, the McClean Report warns that 2028 to 2029 could become a critical window for this AI semiconductor boom. If AI demand growth slows while the large amount of capacity invested in recent and future years comes online, the market may again face oversupply and rapid price corrections.

However, this cycle also has a variable different from the past: whether AI inference demand can continue to spread.

If AI inference workloads expand from current large data centers to consumer electronics, enterprise applications, industrial equipment, and edge computing, even as new fabs, packaging plants, and memory capacity come online, global semiconductor demand may remain relatively high. In other words, whether AI can evolve from 'training-driven' to 'training plus inference-driven' will be a key factor in determining whether this semiconductor cycle can continue.

The current market structure may also further widen the gap between companies. Firms with long-term supply agreements, stable capacity, and advanced process and packaging capabilities will find it easier to maintain product delivery in tight supply environments. In contrast, companies highly dependent on the spot market or bulk memory procurement may face rising costs, extended lead times, and product delays.

Therefore, $1.7 trillion may only be the starting point of this semiconductor expansion. What truly matters is not whether the market can surpass $2 trillion, but when a new balance will be restored among AI demand, memory prices, and new capacity.

If AI infrastructure investment continues to grow rapidly, the semiconductor industry may enter a new long cycle dominated by AI computing demand. But if suppliers' expansion pace ultimately exceeds AI demand growth, 2028 to 2029 could become a period of cyclical correction following concentrated capacity releases.

For the semiconductor industry, this represents a shift in its role. Semiconductors are no longer just a 'component industry' following the cyclical trends of end markets like PCs and smartphones, but are gradually becoming a strategic industry essential to AI infrastructure, data centers, energy, and digital economic development. The real test in the coming years will be whether industry players can find a balance between explosive AI demand and excessive supply expansion.