Article:
Why Samsung’s Profit Tumbles as It Struggles to Catch Up in the AI Chip Race
Samsung’s semiconductor division saw operating profit plunge by 55% in Q2 2025, driven primarily by delays in next-generation AI chips and inventory write-downs. This steep decline exposes three core challenges: lagging behind in the expanding AI chip market, losing ground in High Bandwidth Memory (HBM), and facing geopolitical headwinds that erode sales. In this article, we explain the global AI chip market drivers, dissect Samsung’s technical and competitive gaps, compare HBM market shares, examine financial and policy constraints, outline Samsung’s recovery blueprint, and map key competitors’ strengths. By following this roadmap, readers will grasp why Samsung’s profit tumble reflects broader shifts in AI semiconductors and how the company plans to reclaim leadership.
What Is Driving the Global AI Chip Market Growth?
How fast is the AI semiconductor market expanding?
The AI semiconductor market grows at a compound annual growth rate (CAGR) of roughly 24–28%, driven by surging demand for training and inference workloads. In 2024 the market topped USD 123 billion, and by 2029 it is projected to exceed USD 311 billion, reflecting rapid adoption in cloud, enterprise and edge applications.
AI Chip Market Growth
The AI semiconductor market is experiencing rapid expansion, with a projected compound annual growth rate (CAGR) of 24.4% between 2024 and 2029, driven by increasing demand for training and inference workloads in cloud, enterprise, and edge applications [4]. The market is expected to exceed USD 311 billion by 2029 [4].
This source supports the claim about the AI chip market’s growth rate and projected size.
Introduction of generative AI models and acceleration of data-intensive analytics have accelerated purchases of GPUs, NPUs and custom ASICs. As enterprises invest in AI-powered services, chip makers race to scale wafer capacity and design specialized accelerators.
What role do AI chips play in data centers and edge devices?
AI chips power two critical segments:
- Data centers require high-performance processors for training large-scale machine learning models with teraflops-level compute.
- Edge devices use low-power NPUs and microcontrollers to perform on-device inference for smartphones, IoT sensors and autonomous vehicles.
These segments drive memory bandwidth, power efficiency and packaging innovation, with data centers demanding multi-chip modules and edge platforms emphasizing energy-optimized architectures. Together they form the backbone of AI-driven services worldwide.
Which companies lead the AI chip market today?
The AI chip market is concentrated among a few specialists:
This oligopoly extends to advanced packaging and 2 nm transistor manufacturing, where scale and process maturity dictate who can supply leading-edge AI compute.
Why Is Samsung Struggling in the AI Chip Race?
What caused Samsung’s 55% profit decline in Q2 2025?
Samsung’s semiconductor profit drop stems from a combination of delayed HBM3E qualification, excess inventory in slow-moving DRAM, and under-utilized foundry capacity. AI chip revenue fell short of projections, forcing a 4.7 trillion won operating profit—down from 10.4 trillion won a year earlier.
Inventory writedowns and price erosion in commodity memory exacerbated losses. Weak demand in China amid export controls further reduced wafer utilization, compounding the financial impact of AI-related setbacks and operational charges.
How does Samsung’s HBM3E performance compare to competitors?
Samsung’s HBM3E chips achieved limited yields and failed to meet key GPU maker speed and latency targets, while rival SK Hynix qualified its HBM3E modules ahead of schedule. Samsung’s peak bandwidth falls 10–15% short of peer products, leading to lower adoption in flagship AI GPUs.
This performance gap delays volume shipments and reduces Samsung’s bargaining power in pricing and long-term supply contracts.
What technical hurdles affect Samsung’s HBM stacking and yields?
Advanced memory stacking demands precise wafer-to-wafer alignment, hybrid bonding and tight thermal management. Samsung faces:
- Variability in micro-bumps that raise defect rates
- Bond-line voids impacting conduction and yield
- Stringent warpage control for high-die stacks
Overcoming these challenges requires process refinements in bonding equipment calibration and enhanced in-line inspection, delaying mass production ramp-up.
How is Samsung’s foundry business trailing TSMC in advanced nodes?
Samsung’s foundry lags in yield maturity for its 3 nm GAA (Gate-All-Around) process, while TSMC’s N3 node outperforms in wafer throughput. Key factors include:
- Lower transistor density stabilization
- Higher defect per million yield curves
- Fewer ecosystem partners for design IP
Despite securing a Tesla AI chip deal, Samsung’s overall logic capacity utilization remains below 60%, limiting profitability compared to TSMC’s near-full utilization of advanced nodes.
How Does Samsung’s Market Share in High Bandwidth Memory (HBM) Compare?
Who leads the HBM market and why?
SK Hynix commands over 60% of HBM shipments due to early collaboration with leading GPU makers and a proven stacking process.
HBM Market Share
SK Hynix currently leads the High Bandwidth Memory (HBM) market, holding a 62% share of the market in Q2 2025, while Samsung’s share is under 20% [22]. This is due to early collaboration with leading GPU makers and a proven stacking process [22].
This source provides specific market share data for HBM, supporting the claims about SK Hynix’s dominance and Samsung’s position.
Micron holds roughly 20%, while Samsung lags with under 20% share due to qualification delays and yield constraints.
What is Samsung’s current HBM market share and shipment volume?
Samsung shipped approximately 85 thousand HBM modules in Q2 2025, representing 17% of total market volume compared to SK Hynix’s 310 thousand and Micron’s 105 thousand.
Samsung’s lower volume and share reflect lingering yield issues and slower qualification cycles, necessitating roadmap acceleration.
What innovations are in Samsung’s HBM4 roadmap?
Samsung’s next-generation HBM4 aims to:
- Double per-stack bandwidth via 3D-TSV enhancements
- Embed AI-centric error-correction logic for reliability
- Adopt hybrid bonding to reduce interposer thickness and power
- Integrate embedded power regulators for thermal efficiency
These features target 50% higher module throughput and 20% lower power per bit, positioning Samsung to reclaim share if yields improve.
Samsung’s HBM4 Roadmap
Samsung is developing HBM4, with the goal of completing development by the second half of 2025 [2]. The HBM4 aims to double per-stack bandwidth and integrate AI-centric error-correction logic [2].
This source supports the information about Samsung’s HBM4 development timeline and its features.
What Financial and Geopolitical Factors Impact Samsung’s Semiconductor Performance?
How do US export controls affect Samsung’s China sales?
US restrictions on advanced node equipment and memory sales to Chinese customers reduce Samsung’s addressable market by an estimated 15%. Compliance costs and license delays hinder volume shipments, forcing reallocation of capacity to lower-margin regions.
Stricter checks on HS codes and end-use declarations slow order processing, eroding customer confidence and contributing to inventory buildups.
What role do inventory adjustments and low utilization rates play?
Excess DRAM inventory drives price erosion, prompting Samsung to record additional markdowns of around 800 billion won in Q2 2025. Concurrently, foundry utilization at sub-60% capacity triggers idle cost charges, reducing operating margin.
These operational inefficiencies compound the earnings shortfall from delayed AI chip ramps.
How has Samsung’s semiconductor division profit trended recently?
Semiconductor operating profit fell from 6.5 trillion won in Q1 2025 to 4.7 trillion won in Q2, down 55% year-on-year. Memory revenues declined 30%, while foundry services stabilized but remained below break-even levels for advanced node production. Reduced ASPs (average selling prices) in DRAM and NAND further squeezed margins, marking the third consecutive quarter of profit contraction.
What Is Samsung’s Strategy to Recover in the AI Chip Market?
How is Samsung investing in next-generation HBM and advanced packaging?
Samsung commits over USD 5 billion in 2025 to HBM4 R&D and packaging facilities that support hybrid bonding and chiplet integration. Key initiatives include:
- Building pilot lines for 3D-TSV refinement
- Upgrading test-and-burn-in infrastructure for multi-die stacks
- Partnering with equipment vendors on novel alignment optics
This investment aims to boost yields above 80% by late 2026, enabling competitive volume shipments.
What efforts are underway to expand Samsung’s foundry clientele?
Samsung pursues new design wins by offering competitive pricing at 5 nm and 3 nm nodes for AI accelerators. Notable actions:
- Secured Tesla’s A16 AI processor contract
- Engaged with automotive and networking chip designers
- Launched a co-development program for startups in edge AI
By diversifying its customer base beyond marquee GPU firms, Samsung seeks to improve capacity utilization and amortize advanced node costs.
How does Samsung’s broader AI ecosystem support chip innovation?
Samsung leverages its Galaxy AI platform, SmartThings connectivity and on-device NPUs to:
- Validate AI workloads at scale in consumer devices
- Feed performance telemetry back into fabrication process tuning
- Demonstrate joint system-level optimization of SoCs with memory modules
This cross-product synergy accelerates chip verification cycles and showcases Samsung’s end-to-end AI capabilities to enterprise partners.
Who Are Samsung’s Main Competitors in the AI Chip and Semiconductor Industry?
What advantages do Nvidia and SK Hynix hold in AI chips and HBM?
Nvidia’s GPU architecture combines the largest AI developer ecosystem with tight integration of HBM modules, delivering unmatched performance in training and inference.
Nvidia’s GPU Architecture
Nvidia’s GPUs are designed for massive parallel computations, making them efficient for AI and deep learning [8]. Modern GPUs have specialized cores, including RT cores and tensor cores, to accelerate specific workloads [13].
This source supports the information about Nvidia’s GPU architecture and its optimization for parallel processing.
SK Hynix benefits from:
- Proven HBM3E stacking yields above 85%
- Long-standing partnerships with top AI hardware designers
- Economies of scale in DRAM production that lower unit costs
These strengths reinforce their leadership and create high switching costs for major AI system integrators.
How does TSMC dominate the logic semiconductor foundry business?
TSMC leads through relentless node advancement—offering matured 3 nm and early 2 nm processes with >80% yields—and a broad IP library that accelerates customer tape-out. Its wafer fabs operate near full capacity, driving down per-unit costs and enabling rapid subcontracting for AI ASICs.
TSMC’s Foundry Dominance
TSMC leads in the logic semiconductor foundry business through advanced node advancements, offering matured 3 nm processes with high yields [16]. TSMC’s 3nm FinFET technology was the first to move into high-volume production [16].
This source supports the claim about TSMC’s leadership in advanced node manufacturing and its 3nm technology.
What is Micron Technology’s role in the HBM market?
Micron holds the #2 position in memory by combining DRAM and HBM manufacturing under one roof. Its HBM2E and early HBM3 offerings gained traction with networking ASIC vendors and HPC customers, carving out a niche behind SK Hynix and challenging Samsung’s share in data-center GPU memory supply.
What Are the Key Questions About Samsung’s AI Chip Challenges?
Why is Samsung struggling to qualify HBM3E chips with Nvidia?
Samsung’s HBM3E runs at 6.4 Gbps per pin but exhibits higher latency and lower endurance under stress tests, causing key GPU makers to postpone qualification until yields and performance stabilize.
How significant is Samsung’s profit decline due to AI chip delays?
Approximately 40% of the 55% operating profit fall—equivalent to 2 trillion won—can be directly attributed to delayed AI memory and accelerator launches, underscoring the financial stakes of missing the AI boom.
What is High Bandwidth Memory and why is it critical for AI?
High Bandwidth Memory uses vertically stacked DRAM dies interconnected via through-silicon vias (TSVs) to deliver multi-hundred-GB/s bandwidth per module, enabling AI processors to feed massive data streams for real-time model training and inference.
How will Samsung’s AI chip market position evolve by 2029?
If Samsung achieves target yields on HBM4 and secures additional foundry customers at advanced nodes, its AI memory share could climb above 30% and logic wafer revenue could grow by 3×, restoring competitiveness against entrenched rivals.
Samsung’s profit slump highlights the intricate interplay of technology, capacity and geopolitics in the AI semiconductor landscape. By accelerating HBM innovation, diversifying foundry partnerships and leveraging its AI ecosystem, Samsung aims to reverse its recent setbacks and reclaim a leading role in powering the next wave of AI applications.