Samsung’s record Q3 forecast signals a powerful AI memory boom, as surging demand for HBM, DRAM, and NAND tightens supply, lifts prices, accelerates investment, and intensifies semiconductor competition. 

 

Samsung Electronics’ preliminary third-quarter 2026 earnings guidance has delivered a major earnings surprise, highlighting the extraordinary strength of the semiconductor industry’s current cycle. The company expects approximately KRW 107.4 trillion ($80.2 billion) in operating profit, nearly nine times higher than a year earlier. Revenue is forecast at approximately KRW 195 trillion ($145.6 billion), reflecting strong business momentum. While these figures underline Samsung’s financial performance, they also point to a broader shift across the global technology industry, as surging AI infrastructure investment drives demand for memory chips and strengthens semiconductor pricing. 

 

Samsung’s forecast therefore represents more than an individual company earnings story. It signals that the expansion of artificial intelligence is creating a powerful new demand cycle for the memory-chip industry. As technology companies and cloud providers continue investing heavily in AI infrastructure, semiconductor manufacturers are responding with greater production, capacity expansion and technology investment. This transition is reshaping the competitive landscape and positioning memory chips as an increasingly important foundation of the global AI ecosystem. 

 

The global Memory Chip Market is projected to grow from USD 147.07 billion in 2025 to USD 228.17 billion by 2035, expanding at a CAGR of 4.49% during 2025–2035. The market is being supported by rising demand for AI and machine learning, data centres, cloud computing, consumer electronics, and advanced memory solutions such as DRAM and NAND Flash. 

 

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Samsung’s Record Q3 Forecast Highlights the AI Memory Boom 

Samsung Electronics’ preliminary third-quarter 2026 earnings guidance highlights the accelerating strength of the AI-driven semiconductor cycle. The company’s quarterly revenue is expected to increase by approximately 127% year over year, reflecting a sharp improvement in overall business performance amid stronger semiconductor demand and pricing conditions. Average DRAM prices also increased by around 20% sequentially during the third quarter, indicating that tightening supply is supporting higher memory-market prices. 

 

Rising memory prices, stronger server demand and growing HBM requirements provide important context for Samsung’s record earnings and indicate that the current AI investment cycle is extending beyond processors into the broader memory supply chain. 

 

What Is Driving the New AI Memory Chip Boom? 

AI systems require enormous amounts of high-speed memory because the accelerators that power them must process and continuously move very large volumes of data. Memory bandwidth and capacity have become critical to system performance because insufficient memory access can limit the utilization of expensive AI accelerators. As AI models become larger and are deployed more widely, memory is shifting from a supporting component to a strategic part of AI infrastructure. Demand now extends across several layers of an AI data centre: 

 


AI Accelerators: Training and inference processors require increasingly high memory bandwidth and capacity to handle larger and more complex AI workloads. 


HBM: High-bandwidth memory uses vertically stacked DRAM dies and is integrated closely with AI accelerators to provide very high data-transfer rates while supporting demanding computing workloads. 


Server DRAM: AI servers also rely on conventional DRAM to support host processors, operating systems, data processing and inference workloads, creating additional demand for high-capacity server memory. 


NAND: Enterprise SSDs provide high-capacity storage for training datasets, model checkpoints, applications and the growing volume of data generated by AI workloads. 

Together, these technologies are becoming essential to AI infrastructure, with HBM emerging as the most strategically important memory component for high-performance AI computing. 

 

HBM Becomes the Centre of AI Memory Demand 

High-bandwidth memory, or HBM, has become a critical technology connecting AI processors with the memory industry. HBM uses vertically stacked DRAM dies and a wide interface to provide very high data-transfer bandwidth while being positioned close to AI processors. This allows AI accelerators to access large volumes of data efficiently, making HBM increasingly important for demanding AI training and inference workloads. 

 

Demand is rising as AI accelerator platforms become more powerful and require greater memory bandwidth and capacity. The industry is moving from HBM3E toward HBM4, the next generation of HBM technology, for next-generation AI systems. Samsung began commercial HBM4 shipments in February 2026 and has expanded its HBM4 activities through collaborations with major AI-chip companies. In the second quarter of 2026, Samsung accounted for approximately 33% of global HBM revenue, up from 21% in the first quarter, while SK hynix held 50% and Micron 18%. 

 

The next stage of HBM development is also focused on higher bandwidth, greater capacity and improved energy efficiency. Samsung has introduced HBM4E technology for future AI systems, highlighting the industry’s move toward increasingly advanced memory architectures. As AI models, accelerators and data centres continue to scale, HBM is becoming a strategic component of the semiconductor industry and an increasingly important growth area for memory manufacturers. 

 

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Tight Memory Supply Creates a New Semiconductor Cycle 

AI demand is growing faster than memory manufacturers can add effective production capacity. Expanding semiconductor output requires major investment in fabrication equipment, process upgrades and advanced packaging, while HBM production also uses substantial DRAM wafer capacity. As manufacturers prioritize HBM and server memory for AI infrastructure, conventional DRAM availability can tighten. This shift is creating supply pressure across the broader memory market and increasing the importance of capacity planning and technology investment. 

 

Memory pricing is already reflecting these conditions. Conventional DRAM contract prices are expected to rise 10–15% sequentially in the fourth quarter of 2026, while NAND Flash prices are projected to increase 15–20% during the same period. Memory supply is expected to remain tight as manufacturers gradually add capacity, while the timing of market rebalancing may differ across DRAM and NAND. This suggests that the AI-driven memory cycle could remain influential beyond 2027, although pricing growth may moderate as new capacity comes online. 

 

AI Data Centres Are Reshaping Memory Consumption 

The rapid expansion of AI data centres is creating sustained demand for advanced memory across the technology infrastructure. AI servers increasingly deploy multiple accelerators that require high-bandwidth memory to process large and complex workloads efficiently. At the same time, these systems need substantial server DRAM for data processing, model serving and inference operations. Growing investments by cloud providers and technology companies in AI infrastructure are therefore supporting long-term demand for HBM, server DRAM and enterprise storage as AI workloads continue to scale. 

 

Why DRAM and NAND Prices Are Rising 

Memory prices are being pushed higher by strong demand from AI servers and data centres, limited near-term supply growth and the reallocation of production toward HBM and server-oriented products. As manufacturers prioritize higher-value AI memory, conventional DRAM and NAND availability remains under pressure, creating broader pricing momentum across the memory industry. 

 

The scale of the pricing cycle is reflected in global DRAM revenue, which reached approximately $97 billion in the first quarter of 2026, increasing 80% sequentially and 260% year over year. Higher memory prices and strong AI infrastructure demand were key contributors to this sharp expansion, highlighting the growing financial importance of memory within the semiconductor industry. 

 

For memory manufacturers, higher selling prices can significantly improve revenue and profitability when demand remains strong. For smartphone, PC and other electronics companies, however, rising memory costs increase component expenses and can pressure margins or lead to higher device prices. This creates a growing contrast between strong AI-related demand and more price-sensitive consumer markets. 

 

The Impact on Smartphones, PCs and Consumer Electronics 

The memory boom is also creating pressure across smartphones, PCs and other consumer electronics, as higher DRAM and NAND costs increase component expenses and reduce manufacturers’ ability to absorb rising costs. The latest industry outlook projects global smartphone shipments to decline by 16.7% in 2026 to just over 1 billion units, while the average smartphone selling price is expected to rise 27.6% to $581. Memory costs are rising by more than 300% year over year, putting greater pressure on price-sensitive devices and encouraging manufacturers to focus on higher-value products. 

 

Samsung’s Position in the AI Memory Race 

Samsung Electronics is one of the world’s largest memory-chip producers, with strong positions across DRAM, NAND and high-bandwidth memory (HBM). Its AI-memory strategy is centred on expanding HBM4 production and strengthening its position against SK hynix and Micron. Samsung began commercial HBM4 shipments in February 2026, while its global HBM revenue share reached approximately 33% in Q2 2026, compared with 50% for SK hynix and 18% for Micron. The company is also developing HBM4E and expanding semiconductor manufacturing capacity in South Korea. Securing long-term AI-memory customers and improving HBM production capabilities will be critical for Samsung to gain market share as AI infrastructure investment accelerates. 

 

Key Growth Drivers of the AI Memory Chip Market 


Expansion of AI Data Centres: Rapid investment in AI-focused data centres is increasing demand for high-capacity and high-bandwidth memory across servers and AI accelerator systems.  


Increasing HBM Adoption: Wider adoption of HBM in advanced AI accelerators is driving demand for higher-capacity, faster and more energy-efficient memory solutions.  


Growth of AI Accelerators: The expanding deployment of GPUs, custom AI chips, and other accelerators is increasing memory requirements for AI training and inference workloads.  


Generative AI Workloads: Larger models, longer context windows and multimodal applications are increasing memory capacity and bandwidth requirements across AI infrastructure.  


Rising Server DRAM Requirements: AI servers require greater DRAM capacity to support large datasets, model processing and increasingly complex computing workloads. 

 

Competitive Landscape 


Samsung Electronics: Samsung is a leading global memory-chip producer, spanning DRAM, HBM and NAND. It began commercial HBM4 shipments in February 2026 and is expanding HBM activities through collaborations with major AI-chip companies.  


SK hynix: SK hynix remains the leading HBM supplier, holding a 50% global HBM revenue share in Q2 2026. Its leadership in high-bandwidth memory positions the company strongly as AI data-centre investment increases and demand for advanced memory continues to expand. 


Micron Technology: Micron is a major U.S. memory producer expanding its HBM capabilities and advanced memory portfolio. Its growing investment in high-bandwidth memory positions the company as a key competitor to Samsung and SK hynix in the expanding AI-memory market. 


Kioxia: Kioxia is a major NAND flash and enterprise-storage supplier benefiting from growing AI data-centre requirements. Its latest BiCS FLASH technology is designed to deliver higher storage density, performance and efficiency for data-intensive workloads.  


SanDisk: SanDisk operates as an independent memory company following its separation from Western Digital. Its 332-layer BiCS10 NAND technology targets higher-density storage, improved performance and greater power efficiency for enterprise and data-centre applications.  

 

Regional Development 


South Korea: Samsung and SK hynix are expanding AI-memory manufacturing across Pyeongtaek, Cheongju and Yongin, reinforcing South Korea’s position as a major global center for HBM and advanced memory production. 


United States: The U.S. remains a major centre of AI infrastructure demand and is attracting new memory investment, including SK hynix’s Indiana facility for HBM production and advanced packaging targeted for 2029. 


Taiwan: Taiwan remains critical to AI semiconductor manufacturing through TSMC’s advanced-packaging ecosystem, particularly CoWoS technology, which integrates AI processors with HBM stacks for high-performance computing. 


Japan: Japan remains an important NAND manufacturing hub, with Kioxia and SanDisk expanding advanced 3D flash production at the Yokkaichi and Kitakami facilities to meet growing AI and data-centre storage demand. 

 

Future Outlook 

The AI memory market is entering a structurally stronger growth phase as increasingly complex AI models require greater memory capacity, bandwidth and processing efficiency. Future demand will extend from HBM and server DRAM to high-capacity enterprise storage as AI training and inference workloads expand. Memory manufacturers are expected to invest in advanced process technologies, production capacity and packaging to meet these requirements. Stronger integration between AI accelerators and memory could further improve system performance, supporting sustained opportunities across the semiconductor supply chain. 

 

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Conclusion 

Samsung’s record third-quarter outlook highlights how rapidly AI is transforming the global memory-chip industry. Growing demand for HBM, server DRAM and enterprise storage is strengthening the semiconductor cycle while tighter supply is supporting higher memory prices. The impact is also spreading to smartphones, PCs and other electronics through rising component costs. As AI infrastructure continues to expand, investment in advanced memory technologies, manufacturing capacity and packaging is expected to remain strong, creating long-term opportunities across the global AI semiconductor ecosystem.