The Information: SpaceX and Cursor are set to release their first jointly developed AI model as soon as Wednesday.

“It is expected to be competitive with Anthropic’s Opus 4.8 and OpenAI’s GPT-5.5 in some respects.”

SK hynix 3Q25 Earnings Call – Key Q&A

1. Details on the completion of 2026 HBM supply negotiations

This year, numerous external variables made it difficult to finalize not only supply volumes but also product mix. As customer performance requirements evolved, it took longer than initially expected.

However, discussions with customers have now been concluded, and supply contracts for next year’s HBM have been finalized. Since 2023, demand for AI infrastructure and the company’s strong product competitiveness have kept HBM fully sold out. Prices are currently set at a level that allows profitability to be maintained.

Given the rapid expansion of HBM demand driven by the AI market, it will be difficult for supply to catch up with demand in the near term. Growth is expected to outpace general DRAM. Even in 2027, supply is projected to remain tight relative to demand. The company will ensure timely delivery of products tailored to customer needs.

2. On customer requests for higher HBM4 specifications

As the AI inference market expands, memory bandwidth has become increasingly important. Since the number of I/O channels in HBM4 has been set to double that of the previous generation, customer requirements have shifted toward higher-speed performance.

With the industry’s leading HBM technology, the company is meeting these enhanced specifications. It was among the fastest in the industry to deliver samples reflecting customers’ upgraded requirements and is now prepared for mass production.

As competition intensifies among AI chipmakers, memory performance has become the key bottleneck in technological progress. Performance requirements for next-generation memory, including HBM, will continue to rise. SK hynix plans to deliver next-generation HBM products in a timely manner that meet customer expectations.

3. Achieving over KRW 10 trillion in quarterly results—how is this memory cycle different from past booms?

Contrary to earlier expectations, memory demand has surged this year, pushing the market into a super-boom. We believe the current cycle differs from the 2017/2018 cycle.

The key difference is that today’s demand is tied to a paradigm shift toward AI and is connecting to a wide range of applications. AI is creating demand by being layered onto existing use cases, and over the medium to long term it is driving fundamental change by cultivating new applications such as autonomous driving and robotics.

Notably, as AI computing expands into inference, it is also stimulating demand for general servers. We expect server system shipments next year to grow in the high-teens percent. Server-bound demand will continue to lead overall DRAM demand.

On the supply side, even as HBM production expands and more cleanroom space is secured, total output growth is inherently limited. These characteristics structurally constrain DRAM industry supply growth, which underpins our view that the super-cycle will be prolonged.

4. Basis for seeing structural strength in eSSD demand

Background to rising NAND demand: With server customers ramping AI investments, build-outs for both AI servers and general servers are increasing. Demand for TLC products is rising. As AI-generated data such as images and videos surges, storage needs are climbing, creating HDD supply shortages. Hyperscalers are increasingly shifting to high-capacity QLC eSSDs. We view today’s demand changes as going beyond short-term supply-demand issues and driving a structural uptrend in eSSD demand.

To overcome the limits of the traditional LLM approach, the importance of RAG (Retrieval-Augmented Generation) is growing. Instead of relying solely on trained data, RAG retrieves documents related to a query from external data before generating the final answer, enabling higher accuracy by incorporating the latest and user-specific data. Implementing RAG requires building external database vector stores, and eSSDs are essential for fast retrieval. We therefore expect storage demand to rise on the back of high-performance TLC and high-capacity QLC eSSDs.

In addition, as data throughput needs during inference surge, there is a growing need to offload data previously handled on the GPU to memory/storage for more efficient AI operations. By offloading the GPU’s KV cache down to SSDs, it’s possible to improve throughput per watt and reduce user response latency. We thus expect storage demand to increase, centered on high-performance TLC and high-capacity QLC eSSDs. As AI use cases proliferate, eSSD demand is rising, and memory demand is broadening from DRAM into NAND.

5. Company’s stance on the shift toward a “specialty market” characterized by pre-orders and post-sales

Certain business segments, including HBM, have transitioned to a pre-order and post-sales model. Due to strong customer demand, we have been securing visibility and responding to customer needs through long-term annual contracts established from the initial agreement stage.

As a result, unlike in the past when the memory industry and our business showed high volatility, predictability has increased, and business stability has also expanded. Starting with HBM4E, custom HBM products are being developed jointly with customers from the early design stages of their GPUs or ASICs. Such long-term and strategic partnerships with specific customers are expected to have a positive impact on the stability and profitability of our memory business.

As memory suppliers prioritize capacity allocation for HBM expansion, constraints are emerging in general memory production. This has led to a shortage in general memory supply amid rising demand. Accordingly, more customers are seeking long-term supply agreements for general memory as well. Some customers are even issuing pre-purchase POs for 2026 to hedge against potential supply shortages.

Given the strength of customer demand and our current capacity, not only HBM but also DRAM and NAND capacities for next year are effectively sold out. We will continue to respond to customer demand with optimal production and sales strategies, and we will keep discussing with customers how HBM-driven market changes will affect general memory.

6. Outlook for 2026 CapEx

With growing confidence in the expansion and monetization of the AI market, global AI companies are competitively investing, driving rapid demand growth across various memory products such as HBM, DDR5, and eSSD. To meet this demand, an increase in memory CapEx is inevitable. Our CapEx next year will rise significantly compared to this year.

Equipment move-in at M15X has begun in earnest, and the facility will be utilized to expand HBM supply. For general DRAM and NAND, we plan to accelerate migration to advanced process nodes to address demand. Additionally, considering the construction of the first phase of the Yongin fab and preparations for the Indiana plant, infrastructure investment will continue to increase.

Even with expanding investment, we will maintain strict CapEx discipline and aim to preserve a stable financial structure.

Citi: The Semiconductor Cycle Still Has Significant Upside Potential

Citi analyst Christopher Danely expects global semiconductor sales to rise 16% in 2025 to reach an all-time high of $731 billion. However, he emphasized that this revenue growth is entirely driven by pricing, while shipment volumes remain well below previous peaks. This indicates that inventory levels are low and the industry still has substantial room for further growth.

According to Citi’s data, the current semiconductor industry’s revenue growth has been primarily driven by the surge in logic chip prices. Citi noted that the average selling price (ASP) of logic computing chips (including AI accelerators) has risen 24% over the past three years, far exceeding the 2% growth rate of the previous decade. The share of logic computing chips in total semiconductor sales has also climbed from 27% in 2020 to 39% in 2025.

Within this segment, logic computing revenue is expanding rapidly at a compound annual growth rate (CAGR) of 53%, rising from about $29.6 billion in 2022 to approximately $106.4 billion in 2025, with its share of total semiconductor sales jumping from 5% to 15%.

The average selling price of logic computing chips is expected to soar from $7.80–$8.50 during 2018–2022 to $26.40 in 2025, representing a 47% compound annual growth rate.

Citi identified NVIDIA’s rapid data center expansion as the key driver behind this transformation. The company’s data center business share of logic chip sales has surged from less than 10% in 2021 to 66% in 2025, while its share of total semiconductor sales has also increased from less than 3% in 2021 to 24% in 2025.

A Semiconductor Genius Who Rejected America’s Courtship, Produces “Terrifying Disciples” in China

Sun Nan (孫楠), a professor at the University of Texas at Austin. Nicknamed a “semiconductor genius,” he is one of the top talents in the field. After graduating from Tsinghua University in China, he went to the U.S. to study abroad and earned his Ph.D. at Harvard. In 2020, at the age of 34, Professor Sun faced two choices: stay in the U.S. or return to China.

If he chose the latter, the opportunity cost was immense. His position at the University of Texas came with an average annual salary of $150,000 and tenure. The other option was his alma mater, Tsinghua University, which offered a competitive salary and research support.

In 2020, Sun rejected America’s temptations and returned to China. He was a prolific researcher in the field of integrated circuit chip design, having been the most prolific author in the world’s top semiconductor design journal, JSSC, between 2014 and 2024.

In an interview with Tsinghua’s campus newspaper, he explained his motivation: “I went to the United States to learn advanced semiconductor technology. I achieved a lot there, but I believed that as far as my abilities reached, it was only right to contribute to China. That is what a Tsinghua graduate should do.”

He added: “China manufactures and exports smartphones, home appliances, and other products. It is the world’s largest exporter. But to actually make those products, China also has to import more semiconductors than any other country. That was something I found regrettable.”

Morgan Stanley HBM Report

• According to channel checks, the contract negotiations between SK Hynix and NVIDIA for HBM3E 12-hi remain in the price range of around $440 per die ($1.69 per Gb). However, the negotiations are still ongoing and have not yet been finalized.
• HBM4 has been priced at $590–600 per die ($2.30–2.34 per Gb), but it is deemed unlikely that this pricing will be part of a full-year fixed contract.
• SK Hynix’s share of NVIDIA’s business is expected to decline from 85–90% in 2025 to just over 50% in 2026, driven by intensified competition from Samsung and Micron.

The HBM That Jensen Huang Called a Miracle Made with Samsung: What Happened Over the Past 9 Years?

In August 2016, a senior engineer from Micron harshly criticized HBM, which had been on the market for just one year, calling it a “cheap knockoff” of the Hybrid Memory Cube (HMC). At that time, SK Hynix’s HBM2 was struggling, and the company was in a difficult financial position.

Today, HBM is hailed as the “prince of memory” in the AI era. The current market shares are approximately 55% for SK Hynix, 40% for Samsung, and 5% for Micron.

HBM was born to solve the “memory wall,” a bottleneck caused by the performance gap between CPUs and memory. Its development is tied to the evolution of the GPU, specifically through early partnerships between AMD and SK Hynix starting in 2010, long before the AI boom made HBM a critical component.

How China Took Over the LiDAR Industry: The Comeback of a "Fool's Tech"

LiDAR (Light Detection and Ranging) is a sensor that emits laser beams to detect surroundings in 3D, serving as a critical component in autonomous driving and consumer electronics. While Tesla famously rejected LiDAR, China has quietly taken a dominant position in the industry.

The industry faced a massive downturn after 2020 when Level 3 autonomous driving failed to materialize as quickly as expected, leading to bankruptcies and mergers for many startups like Velodyne, Quanergy, and Ibeo. Despite the collapse of valuations for companies like Luminar and Innoviz, the technology has remained essential for robotic and vehicle applications, with Chinese firms capitalizing on mass production capabilities while many U.S. and European players struggled to survive.