On the Eve of Its U.S. Journey, SK Hynix Plummets Sharply

Odaily星球日报Опубліковано о 2026-07-02Востаннє оновлено о 2026-07-02

Анотація

Just before its highly anticipated U.S. listing, SK Hynix saw its share price plummet dramatically, losing over 14% in a single day. The sell-off was triggered by market fears of a potential slowdown in AI capital expenditure. This followed a news report suggesting Meta might sell "excess AI compute," which was later amended to remove the word "excess." The initial phrasing sparked a chain reaction in investor sentiment, linking it to a potential peak in AI demand. Despite the sharp downturn, the article argues this is likely an overreaction driven by market sentiment and structural de-leveraging, rather than a fundamental reversal of the AI trend. The author points out that even if Meta proceeds, it could be an optimization of existing assets, not a systemic demand contraction. SK Hynix is in the final stages of its U.S. IPO via an ADR listing on Nasdaq, aiming to raise approximately $29.4 billion—one of the largest such offerings ever. The funds are earmarked for expanding domestic Korean production capacity for HBM (High Bandwidth Memory) and advanced packaging. A key motivation for the U.S. listing is to achieve a valuation re-rating, escaping the so-called "Korea discount" and tapping into the higher valuation multiples typically given to AI-related semiconductor stocks in the U.S. market. In conclusion, the article views the current price drop as a potential buying opportunity, suggesting the long-term industry fundamentals for SK Hynix—particularly its leading posit...

Original | Odaily Planet Daily(@OdailyChina)

Author|Azuma(@azuma_eth)

SK Hynix's process for listing in the U.S. has entered its final stage. However, just as this South Korean memory giant is about to debut on the Nasdaq, the narrative surrounding the AI and semiconductor industries has taken a sharp emotional turn in a remarkably short period.

On the evening of July 1, news that "Meta might release excess computing power" sparked speculation that major tech companies could cut back on capital expenditures, leading to significant market volatility. As the narrative of AI computing power's "absolute scarcity" began to loosen, the semiconductor memory chip sector was directly impacted, causing a substantial collective correction in related stocks in the secondary market — SK Hynix's South Korean stock closed down 14.57%, wiping out over a hundred billion dollars in market value in a single day.

  • Odaily Note: For reference, see "Has the 'Biggest Headwind' for Chip Stocks Finally Arrived? Could Meta Be the First Major Company to Cut Capital Expenditure?".

Countdown to SK Hynix's U.S. Listing

On June 30, SK Hynix submitted its F-1 registration statement to the U.S. Securities and Exchange Commission (SEC), planning to list on the Nasdaq via the issuance of "American Depositary Receipts" (ADRs). The planned fundraising scale is approximately 45.45 trillion Korean won (about $29.4 billion USD), potentially becoming one of the largest ADR issuances in history. The proceeds from this offering will be entirely used for expanding domestic production capacity in South Korea, including the Yongin wafer fab, the Cheongju advanced packaging line, and investments in EUV and related equipment.

  • Odaily Note: An ADR (American Depositary Receipt) is essentially a trading vehicle for non-U.S. companies in the U.S. stock market. An ADR is not a stock directly issued by the company on the U.S. exchange; instead, it is a "substitute security" issued by a custodian bank in the U.S., with the underlying asset being the ordinary shares of the foreign company. Through ADRs, investors can directly trade the shares of overseas companies in the U.S. market in U.S. dollars without the need to open cross-border accounts or handle foreign exchange and settlement processes.

This transaction is jointly underwritten by Bank of America, Citi, Goldman Sachs, and JPMorgan. A total of 17.79 million new shares will be issued (representing 2.5% of its total issued shares), with the stock ticker SKHY. In terms of timing, the ADRs are expected to begin trading on the Nasdaq on July 10.

The fundamental reasons why SK Hynix is actively pursuing its U.S. listing at this point in the cycle are the convergence of three factors: the industry cycle, the capital window, and competitive dynamics.

First, SK Hynix is currently experiencing a historically strong business cycle. Driven by AI server demand, High Bandwidth Memory (HBM) has become the most critical supply bottleneck. The company holds over a 50% market share in this segment, which has simultaneously propelled its overall DRAM business into a high-profit phase. This has also led its performance and stock price into an upward trajectory, creating a typical "financing window at the peak of the cycle" — raising funds for large-scale capacity expansion during the period of strongest fundamentals.

Second, from a capital market structure perspective, the U.S. market remains the primary pricing center for global AI assets. Whether it's NVIDIA, AMD, or memory chip companies like Micron, the U.S. stock market generally assigns significantly higher valuation multiples and liquidity premiums to AI industry chains. In contrast, the South Korean market has long suffered from the so-called "Korea discount," with similar semiconductor assets generally valued lower than their U.S. counterparts. Therefore, one of the core purposes of SK Hynix's U.S. ADR listing is to seek a re-rating within a higher valuation framework.

Lastly, memory giants are currently engaged in fierce competition for capacity expansion, which heavily relies on continuous massive capital investment. SK Hynix's nearly $30 billion USD fundraising will be entirely used for wafer fab expansion, advanced packaging, and equipment capacity increases, essentially aiming to translate capital advantage into capacity advantage.

Falling So Sharply, Is SK Hynix Still Worth Buying?

Initially, SK Hynix's U.S. listing could have been viewed as a historic moment for the memory industry. However, the significant correction that began last night has injected immense uncertainty into its near-term outlook. Is this an opportunity to buy the dip, waiting for a takeoff post-U.S. listing? Or should one decisively reduce positions to avoid a potential bubble burst?

Disclaimer upfront: The following section is purely based on personal opinion and does not constitute investment advice.

In my personal view, this sharp decline of SK Hynix, including the substantial sector-wide correction, resembles a liquidity-driven stampede amplified by sentiment, rather than a fundamental reversal of industry trends.

First, focusing on the news catalyst — "Meta might release excess computing power" — this report itself appears to be overinterpreted.

Bloomberg's original headline when first publishing this news was "Meta Is Building a Cloud Business to Sell Excess AI Compute," but it was later changed to "Meta Is Planning a Cloud Business Sell AI Computing Power." However, other media outlets, including Reuters, had already forwarded and reported using the first headline.

There are two key changes between the two headlines. Firstly, "is building" was changed to "is planning to build," which directly reduces the certainty and timeliness of the report. Secondly, the term "excess" was removed. However, this initial term easily led the market to interpret it as "computing power is already in excess," triggering a chain of reasoning: "excess computing power → capital expenditure peaking → weakening AI demand," ultimately causing market panic.

Taking a step back, even if it's confirmed that Meta intends to sell computing power, it's difficult to constitute sufficient grounds to conclude that the "AI capital expenditure cycle" has ended. From an industrial logic perspective, Meta itself is relatively behind in the AI race. The pressure it faces in foundational models and computing efficiency objectively determines that Meta has some degree of need for computing power scheduling and asset optimization. Against this backdrop, externalizing or commercializing part of its computing resources resembles an act of optimizing asset utilization rather than a systematic contraction on the demand side.

Such "computing power reallocation" is not uncommon in the AI industry chain. Two months ago, SpaceX also commercialized part of its computing resources (e.g., leasing to Anthropic). Essentially, this is a rebalancing act for cost and resource efficiency, not a negation of AI demand itself. Therefore, extrapolating the computing scheduling behavior of a single company, of an uncertain scale, directly to imply "industry-wide excess" represents a clear logical leap.

As for why the impact of this news was so severe, another crucial reason lies in the market structure. Prior to this round of decline, the semiconductor memory chip sector was already at relatively high levels, with concentrated holdings from trend-following funds and leveraged ETFs. Under such a structure, market sensitivity to marginal information significantly increases. Once a narrative shock occurs, it easily triggers amplified deleveraging and passive selling, transforming what might have been a "level of expectation adjustment" volatility into a "price stampede-level" correction.

Therefore, this correction more closely resembles a typical outcome of "sentiment panic + structural deleveraging" superimposed. Personally, I am also inclined to take the opportunity to add positions during this decline.

After all, SK Hynix itself is in the critical window for its U.S. listing. With a fundraising size nearing $30 billion USD, whether it's the underwriters or the institutional funds participating in the subscription, they likely wouldn't want the stock price to perform too poorly after the listing.

Пов'язані питання

QWhat is the main reason for the sharp drop in SK Hynix's stock price right before its U.S. listing?

AThe sharp drop was triggered by market speculation that 'Meta might release excess computing power,' which led to concerns that major tech companies might reduce capital expenditures. This caused a panic and a liquidity crunch, particularly affecting the semiconductor and memory chip sectors, including SK Hynix, whose Korean shares fell 14.57% in a single day.

QWhat is the purpose and scale of SK Hynix's upcoming ADR issuance in the U.S.?

ASK Hynix plans to issue American Depositary Receipts (ADRs) on Nasdaq to raise approximately 45.45 trillion Korean won (about $29.4 billion). The funds will be used entirely for expanding production capacity in South Korea, including the Yongin wafer fab, Cheongju advanced packaging lines, and investments in EUV and related equipment.

QWhy does the article suggest that the news about Meta's potential cloud business was likely overinterpreted by the market?

AThe article argues that the news was overinterpreted because Bloomberg initially used a headline stating 'Meta is building a cloud business to sell excess AI compute,' which was later changed to 'Meta is planning a cloud business to sell AI computing power.' The removal of the word 'excess' and the shift from 'is building' to 'is planning' reduced the certainty and immediacy of the report. The market's leap from this news to assuming an 'AI capital expenditure cycle' had ended was an exaggerated logical jump.

QAccording to the article, what are the three main reasons behind SK Hynix's decision to list in the U.S. at this time?

AThe three main reasons are: 1) SK Hynix is in a historically strong business cycle, driven by high demand for HBM (High Bandwidth Memory) in AI servers, creating an ideal 'cycle high financing window.' 2) The U.S. market offers higher valuation multiples and liquidity for AI-related assets compared to the 'Korea discount' prevalent in its home market. 3) The company needs massive capital to compete in the intense expansion race within the memory chip industry, aiming to convert capital advantage into production capacity advantage.

QWhat is the author's personal investment view on the recent decline of SK Hynix and the semiconductor memory sector?

AThe author personally views the sharp decline as more of an emotional panic and a structural deleveraging event (involving leveraged ETFs and trend-following funds) rather than a fundamental reversal of the industry trend. The author is inclined to see it as a buying opportunity, partly because the large-scale U.S. listing involves major underwriters and institutional investors who would not want the stock to perform poorly post-listing.

Пов'язані матеріали

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: China's AI Models at an Inflection Point China's open-source/open-weight large language models (LLMs) have reached performance parity with top global proprietary models, according to a Goldman Sachs report. This is driven by architectural innovations and higher parameter efficiency, allowing Chinese models to achieve comparable capabilities at 2%-10% the parameter size and significantly lower cost. The market is evolving into a two-tiered structure: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with premium pricing and a low-end, price-sensitive segment for global SMEs and individual users. Key points: * **Cost & Performance:** Innovations like Mixture of Experts (MoE) enable high performance with smaller models. Projects like Meituan's LongCat 2.0, trained on domestic hardware, highlight progress in tech self-sufficiency. * **Open-Source Strategy:** Most Chinese players use open-source/open-weight models for flexibility and ecosystem growth. However, Goldman notes this may underreport actual deployment and revenue. A shift toward "open-weight + community license" models with revenue sharing (e.g., MiniMax) could improve monetization. * **Market Shift & Global Expansion:** Enterprise AI adoption is shifting from "token maximization" to "ROI-first." International expansion, especially in non-US markets, is a major growth driver. Chinese models are increasingly available on global platforms like AWS Bedrock and Microsoft Copilot. * **Competitive Landscape:** Using a framework based on pricing power, cost advantage, and financial strength, Goldman identifies **Zhipu AI and DeepSeek** as the strongest in foundational text models, and **ByteDance** as the leader in multimodal/video generation. The report maintains Buy ratings on MiniMax and Kuaishou. * **Market Growth:** China's AI model API and subscription revenue is projected to grow from an estimated ¥35 billion in 2026 to ¥879 billion by 2030.

marsbit14 хв тому

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

marsbit14 хв тому

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry? China's AI large model sector is at a historic inflection point. Goldman Sachs argues that the intelligence of Chinese open-source/open-weight models is approaching top global proprietary models. Rapid adoption by domestic enterprises and global SMEs is creating a data flywheel effect that will further drive model iteration. The evolution is summarized as moving from "DeepSeek's cost-efficiency moment last year to GLM's model-intelligence moment this year." Chinese models achieve near-state-of-the-art performance at significantly lower cost, primarily due to architectural innovations like Mixture of Experts (MoE) and higher parameter efficiency. Models like DeepSeek V4 Pro (1.6T params), GLM5.2 (0.7T), and MiniMax M3 (0.4T) are much smaller than global leaders. Recent advancements in coding capability are attributed to better data curation and RLHF. Landmarks like Meituan's LongCat 2.0, trained fully on domestic AI chips, demonstrate progress in hardware stack independence. The market is forming a "two-tiered structure." The high-end tier (e.g., GLM5.2, Alibaba's Qwen3.7 Max) prices around $1 per million tokens, about 10-25% of US top models, with estimated inference gross margins of 10-20%. The low-end tier (priced as low as $0.06-$0.2 per million tokens) targets price-sensitive global SMEs and individuals. MiniMax derives 60-70% of revenue overseas. Goldman forecasts China's AI model API/subscription revenue to grow from an estimated RMB 35bn in 2026 to RMB 879bn by 2030. Most Chinese players adopt open-source/open-weight strategies for deployment flexibility and community feedback, though this limits monetization as deployments on third-party platforms (e.g., Alibaba Cloud) may not generate direct revenue. A shift towards "open-weight + community license" models with revenue-sharing agreements (like MiniMax's approach) could improve unit economics. International expansion, particularly in non-US markets, is the key growth driver. The global enterprise AI paradigm is shifting from "token maximization" to "ROI prioritization." Chinese models are already hosted on major global platforms like AWS Bedrock and are under consideration for integration into Microsoft Copilot. Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman identifies the strongest players: In foundational text models, Zhipu AI (initiated coverage) and DeepSeek lead. In multimodal/video generation, ByteDance's Seed is the frontrunner, with Kuaishou's Kling and MiniMax's Hailuo also well-positioned. Goldman maintains a Buy rating on MiniMax, citing its attractive valuation.

链捕手18 хв тому

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

链捕手18 хв тому

Is Ethereum Truly a "World Computer"?

Title: Is Ethereum Really a "World Computer"? Ethereum, envisioned as a "world computer" by its founder Vitalik Buterin, aims to be a decentralized platform for global applications. However, a recent analysis by Four Pillars raises questions about whether it is more accurately a "Western computer," based on the geographical distribution of its validators. Currently, the United States dominates with 38.19% of all validators, followed by Germany at 13.04%. Combined, these two countries account for over half of the network. In contrast, Asian representation is minimal, with Singapore holding only 3.15%. The concentration is partly due to affordable cloud hosting services like Hetzner and OVH in Europe and North America, as well as the prevalence of residential validators in the U.S., where individuals run nodes via home internet connections. When examining professionally operated validators, the distribution becomes more balanced. The U.S. share drops to 25.81%, while Asian countries like Singapore (7.28%), Hong Kong (6.44%), Japan (6.38%), and South Korea (4.59%) collectively approach the U.S. level. This shift reflects strategic deployments by institutions to meet regulatory requirements and reduce latency for local users. However, regions like South America, the Middle East, and Africa remain underrepresented. Ethereum's peer-to-peer network mechanisms, such as gossipsub, disadvantage areas with low node density, creating a feedback loop where delayed message propagation reduces validator performance and rewards. This imbalance challenges Ethereum's promises of censorship resistance and global accessibility. Despite these issues, opportunities exist for growth in underrepresented regions. As demand for localized staking infrastructure rises, early entrants in areas like the Middle East could establish dominant positions by offering compliant, low-latency solutions. The evolving validator landscape highlights both the structural challenges and the potential for Ethereum to move closer to its "world computer" ideal.

Foresight News2 год тому

Is Ethereum Truly a "World Computer"?

Foresight News2 год тому

Торгівля

Спот
活动图片