Summary: Goldman Sachs reiterates Buy on ASML ahead of Q2 2025 earnings; bullish on 2026 growth drivers including leading-edge logic, HBM demand, and DUV recovery.

Goldman Sachs) ASML: Q2 2025 Earnings Preview

ASML is scheduled to announce its Q2 2025 earnings on July 16, and we've summarized key themes and our latest outlook on the company. Our main areas of focus include: (1) key drivers supporting ASML's growth expectations for 2026; (2) the extent to which artificial intelligence (AI) will continue to drive demand for cutting-edge logic/memory chips; (3) the order intake run-rate needed in the coming quarters to meet 2026 market consensus estimates; and (4) the future direction of lithography technology advancements beyond the Gate-All-Around (GAA) architecture transition. We maintain our 'Buy' rating.

1. Key Drivers Supporting ASML's Growth Expectations for 2026

We believe investors are currently focused on the key positive and negative factors supporting ASML's growth expectations for 2026 (for reference, Visible Alpha Consensus Data currently anticipates 8% year-over-year revenue growth). In a recent roadshow we hosted, ASML indicated potential for growth in 2026 driven by: (1) leading-edge logic; (2) advanced memory applications such as High Bandwidth Memory (HBM); and (3) a recovery in investments for trailing-edge processes.

1. Accordingly, we maintain a positive view on ASML's 2026 growth opportunities. This is based on our expectation that leading-edge demand will remain robust due to TSMC's 2nm node expansion, which will be a critical node in multi-year capacity build-outs. Furthermore, we see upside potential to 2026 estimates if other major leading-edge players resolve technical issues and resume capacity expansions.

2. Separately, demand for advanced memory, such as HBM and DDR5, remains robust and could lead to further capacity expansion next year, driving front-end semiconductor equipment demand. In this context, Micron recently stated in its earnings call that a significant portion of its FY25 CapEx is allocated to HBM capacity expansion and that it will continue to expand greenfield capacity by installing equipment for new nodes in the future.

3. In addition, recent data from analog/power semiconductor companies suggest relatively healthy demand outlooks and inventory levels for Western companies, while demand from China continues to be strong. For example, Microchip raised its Q1 FY26 guidance based on better-than-expected performance, with May order intake marking the highest in the past two years. We expect a recovery in investments in trailing-edge capacity to support demand for ASML's (higher-margin) Deep Ultraviolet (DUV) equipment going forward.

4. Finally, investors will likely seek further clarification on potential risk factors related to China revenue. However, we note that current investments by major companies in China are now diversified across a greater number of customers than in the past.

2. To What Extent Will AI Continue to Drive Demand for Cutting-Edge Logic/Memory Chips?

In our view, after very strong build-outs over the past two years, investors are now keen to understand the sustainability of AI-related semiconductor demand. While acknowledging the significant investments in leading-edge semiconductors in recent years, we believe AI can provide a long-term tailwind for cutting-edge semiconductor chips.

1. Firstly, our conviction in the sustainability of AI demand is supported by positive data from the AI ecosystem (see Chart 1), which highlights the diversification of AI applications and the broader adoption of AI. For instance, our TSMC analyst recently made positive revisions to wafer revenue forecasts for 3nm/5nm processes, reflecting a reduction in near-term AI order-related risks.

2. Furthermore, Nvidia stated during its Q1 FY26 earnings call that Microsoft processed approximately 100 trillion tokens in that quarter, a five-fold increase year-over-year. We view the aforementioned as largely consistent with our view that the proliferation of inference models (compared to traditional single inference approaches) will spur computing demand and, consequently, drive demand for cutting-edge semiconductors to power these models.

3. More broadly, we maintain a positive outlook on leading-edge demand for AI applications and believe ASML is well-positioned to benefit from this trend, given its status as the sole supplier of lithography equipment enabling the incremental node shrinkage necessary to make chip performance even more powerful for complex AI workloads.


Summary: Relays JPM investor sentiment on NVDA: buy-side views any pullback as opportunity on 2H/GB300 ramp, though miss risk and slowing flows flag potential 15–20% downside.

structural marker; pre-classified as canonical yfinance ticker

J.P. Morgan Investor Sentiment Analysis: The Difficulty of Interpreting NVIDIA (NVDA) Guidance

Investor sentiment: As evidenced by somewhat confusing buy-side (institutional investor) survey results, this is a particularly difficult quarter to interpret NVIDIA (NVDA)’s guidance. In principle, the China export restrictions on the H20 chip that took effect on April 9 should result in an annual revenue exclusion of approximately $15–16 billion. (For reference, AMD’s case suggests a $7.6 billion impact on both the July and October quarters respectively.) Nevertheless, it appears that most sell-side analysts have not updated their models to reflect the H20-related loss, and many investors we’ve spoken with also indicate they have not incorporated it yet. This is highlighted as a potential risk factor that could drive volatility around the earnings release.

In fact, buy-side consensus is that July quarter guidance will come in approximately $920 million below prior expectations—not the previously cited $7 billion impact. Nearly everyone I’ve spoken to expects Q2 (July quarter) revenue guidance in the range of $45–46 billion. There is also concern over Q1 margin guidance, which will be heavily affected by amortization. About one-third of our survey respondents expect Q1 gross margin (GM) to be around 58% (vs. the sell-side consensus of 71.1%), while the remaining two-thirds are clustered between 65–72%. Reconciling these views is challenging, but here are a few key takeaways from our investor conversations:

1. Many investors are comforted by the view that NVIDIA will ship inventory in anticipation of a strong 2H (i.e., “fill the channel to smooth volatility”). Last Friday’s news involving Oracle (ORCL) highlighted the possibility of unexpected demand upside. In addition, there is growing optimism around ODM yield improvements (Albert Wang put out a great note on this last week). There’s no dispute here—everyone is watching those receivables, and backend yields are improving quickly.

2. Some investors expect NVIDIA to sell some form of chips to China this year—such as the B30 or a custom RTX Pro 6000 server SKU—retaining all or part of the $15–16 billion China revenue as a temporary item. Others suggest that not all of the H20 revenue is lost—some say “none of it”—and that the company may recover a portion of it.

3. One investor proposed that some investors who missed the recent 35% rally are now taking short positions and intentionally circulating higher guidance expectations to set the stage for a disappointment. Short interest has indeed risen in recent weeks (although the name still has a skewed long bias). However, more investors see any pullback as a buying opportunity, noting smoother 2H dynamics and a ramp in GB300 production. Gokul’s sourcing from Computex was positive for both the GB200 ramp and what appears to be a more seamless, higher-demand GB300 ramp.

4. Lastly, one risk flagged by our Delta One desk is that beta appetite in the market appears to be waning, and both retail and institutional flows into NVIDIA are slowing. Historically, when both types of flows and sentiment stall or reverse, NVIDIA’s stock has experienced a 15–20% decline. Our team believes that if NVIDIA misses earnings tomorrow, this could be a meaningful downside risk.


Summary: Citi/Dell'Oro data: datacenter capex +50% YoY, 5M+ AI accelerators in 2025, Blackwell-focused; raises 2025 overall capex growth forecast to 30%.

Datacenter Capex: AI Drives Significant Growth in Datacenter Investment, High-End Accelerator Shipments Expected to Exceed 5 Million Units in 2025

Citi (A. Malik, 06/17/25)

The institution, citing a Dell’Oro report, points out that global datacenter Capex is expected to continue its rapid growth in 2025. AI training remains the primary investment focus, with the top four cloud providers driving the deployment of millions of accelerators.

Global Datacenter Capex in 1Q25 Grew Over 50% YoY, Totaling $134 Billion

The growth was mainly driven by accelerated investment in AI servers, which accounted for over half of the total Capex, surpassing general-purpose infrastructure. Due to anticipated tariff risks, system vendors and hyperscale cloud service providers are stockpiling in advance, leading to price increases for components like NICs and memory. The enterprise market also saw a 21% YoY growth, driven by the server replacement cycle.

The Top 11 Cloud Providers Hold 60% of the Global Market Share, with the Top 4 Growing 73% YoY

In 2025, the Capex for the Top 4 (US-based) providers is expected to grow 39% YoY, leading the entire industry into a multi-year investment cycle expansion. The top four cloud providers plan to deploy over 5 million AI accelerators in 2025, with major investments focused on Blackwell (especially the GB200) and its supporting infrastructure.

Major Cloud Providers are Actively Expanding Datacenters with a Clear Cadence for New Platform Deployment

Microsoft plans a large-scale deployment of its Maia platform in the second half of 2025. META expects to expand to 14 new regions within the next 2-4 years. Amazon and Google are benefiting from cloud service growth, which is driving their general-purpose server deployments. Oracle plans to add datacenters in 7 new regions within the next 12 to 18 months, launching a four-year investment plan totaling $100 billion.

ASICs and Custom Accelerators Gain Attention; Apple Plans to Launch "Baltra" AI Training Chip in 2026

Shipments of custom accelerators like Google's TPU and Amazon's Trainium are expected to reach 2 million units in 2025. Apple is maintaining a hybrid Capex strategy and is rumored to be developing an AI training chip named "Baltra." Enterprise customers, due to the lack of supporting infrastructure like liquid cooling for Blackwell and NVIDIA's one-year update cycle, are increasingly turning to public cloud AI solutions.

High-End Accelerated Server Capex in 2025 is Expected to Reach $205 Billion, Accounting for 34% of Total Capex

Dell’Oro forecasts that high-end AI servers will ship with 5 million accelerators in 2025, corresponding to a server Capex of $205 billion. This represents 34% of the total datacenter Capex and excludes spending on networking and physical infrastructure. The institution (Citi) has raised its 2025 overall Capex growth forecast to 30%.


high-end AI servers will ship with 5 million accelerators in 2025, corresponding to a server Capex of $205 billion

US datacenter sector proxied by DTCR ETF


Summary: Relays supply-chain report that NVDA and AMD will launch China-compliant AI GPUs from July 2025, with both set to benefit significantly in revenue; AMD seen as potentially bigger beneficiary.

Report: AMD to Follow NVIDIA in Launching Downgraded AI Chips for China – Digitimes

According to sources in the semiconductor supply chain, NVIDIA and AMD have swiftly adjusted their downgraded chip designs and are expected to launch a new series of AI GPUs that comply with export regulations to China starting from July 2025.

Reportedly, NVIDIA is preparing a product tentatively named the B20, while AMD plans to compete in the market with products such as the Radeon AI PRO R9700. These chips are capable of running models like DeepSeek, and interest from Chinese customers is said to be high. Both companies are likely to benefit significantly in terms of revenue.

The U.S. government has strengthened AI chip export restrictions citing national security concerns. Before leaving office, President Biden introduced a three-tier export control policy. Following his return to the presidency, Donald Trump immediately resumed trade wars, strictly enforced origin tracing rules, and pushed for a “Made in America” agenda, further tightening bans including the export of NVIDIA’s H20 chip to China.

Although Trump recently withdrew the three-tier AI chip export control policy and warned that any company using Huawei’s Ascend AI chips would be in violation of U.S. export controls, NVIDIA—once holding over 90% market share in AI GPUs—has seen its share in China drop significantly following the H20 ban.

In response, NVIDIA CEO Jensen Huang has publicly stated multiple times that the Trump administration should reconsider its export control policies, arguing that these measures have not achieved their intended goals.

Four years ago, when the Biden administration first took office, NVIDIA's market share in China was 90%, but it has now fallen to around 50%. The original intent of slowing down China's AI development has, in reality, backfired.

China currently holds 50% of the world’s AI researchers, making it the largest concentration of AI talent globally. The country remains steadfast in its commitment to advancing AI.

Jensen Huang recently emphasized the innovations and contributions of China’s DeepSeek, saying the model provides tangible global value and praising Alibaba’s Qwen3 model as well. He stated confidently that China’s AI research will continue to thrive.

He also warned that if NVIDIA were forced out of the Chinese market, other companies like Huawei would quickly fill the void.

All countries desire to lead in AI, and even Trump is reportedly aware that the previous administration’s AI containment policies have failed. Jensen Huang believes that export control and AI containment policies must be reversed together, and while that hasn’t happened yet, he expressed hope that changes will come.

NVIDIA’s repeated emphasis on the importance of the Chinese market has fueled speculation that it will once again launch new downgraded chips designed specifically for China.

Jensen Huang stated that it is no longer feasible to redesign the H20 or existing products to comply with the restrictions, and as a result, NVIDIA decided to write down $5.5 billion worth of chip inventory. This figure surpasses the annual revenue of many semiconductor firms, representing not only a major revenue loss for NVIDIA but also a significant tax loss for the U.S. government.

NVIDIA is currently working on new strategies. While there is no clear direction yet, the company’s commitment to the Chinese market remains unchanged.

Sources in the supply chain say that the Chinese market remains crucial for both NVIDIA and AMD. Although AMD has historically had a much smaller presence in the AI GPU space compared to NVIDIA, its shipments have recently grown noticeably.

The launch of downgraded chips could thus represent an even bigger opportunity for AMD, potentially allowing it to compete head-on with NVIDIA starting in July, with NVIDIA’s B20 (based on the Blackwell architecture) and AMD’s Radeon AI PRO R9700 both entering the Chinese market.

These chips will feature significant adjustments to memory and compute performance, and are designed for AI workstations, optimized for local inference, model fine-tuning, and other data-intensive workflows.

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https://t.co/g3YPmrRr6K


The launch of downgraded chips could thus represent an even bigger opportunity for AMD


Summary: UBS: NVDA may benefit from loosened export controls as Trump trades restrictions for China concessions on critical minerals.

Tariffs: Section 232 Investigation Results Potentially in July-August, Electronic Product Tariffs Most Likely, Export Control Relaxation and Material Restrictions as Potential Variables
UBS (T. Arcuri, 2025/06/29)

UBS points out that tariff policies will become a blind spot for investors in the next 4-6 weeks. The Section 232 investigation may conclude earlier, in July or August, with the focus likely shifting to imposing a 25% tariff on finished electronic products and introducing a mechanism for deducting the content of U.S.-produced products. Concurrently, export controls might see marginal relaxation, but restrictions on materials could become a new focal point in the U.S.-China competition.

Section 232 Tariff Investigation Pace Significantly Accelerated, Final Measures Expected in July-August

Experts indicate that the Trump administration has recently accelerated the semiconductor-related Section 232 investigation. The originally anticipated 9-month investigation period has been compressed, and final policies might be released in late July to early August. This accelerated pace is notably earlier than market expectations and could trigger a short-term policy shock reaction in the market.

25% Tariff on Finished Electronic Products Expected as Main Outcome, No Product Content Exemption Initially

Experts emphasize that the most probable outcome of the investigation is a uniform 25% tariff on finished electronic products, rather than on semiconductors themselves. This policy will be superimposed on existing country-specific tariffs and will initially not include exemptions for U.S.-produced product content or exceptions for Taiwan, due to high administrative enforcement difficulty. In the long run, a deduction mechanism might be gradually introduced, but no exemptions are expected in the short term.

Export Controls Might Loosen, NVDA Potentially a Beneficiary

Experts believe that the Trump administration might be more willing to relax export controls in exchange for China's concessions in areas like critical minerals. For instance, EDA and aerospace components have already become bargaining chips, suggesting that companies like NVDA might secure more exemptions on computing power export restrictions in the future, especially under a larger trade agreement framework.

AI Diffusion Rules Potentially Replaced, Bilateral AI Cooperation Framework More Likely to Land

Experts note that the government has not strongly pushed for AI Diffusion rules. The Trump administration is more likely to replace relevant regulations with bilateral AI cooperation agreements between countries to support AI infrastructure collaboration with the UAE and Saudi Arabia. However, these agreements currently resemble frameworks of intent rather than substantive supply agreements, leaving uncertainty about their actual implementation.

Total Ban on Semiconductor Equipment Unlikely, Material Segment Becomes New Potential Chokepoint

Experts indicate that a comprehensive ban on U.S. semiconductor equipment exports to China is extremely difficult and would require multi-country coordination, making it unrealistic. A more feasible approach is to expand the scope of the export controls from October 7, 2022, to include 40-65nm legacy process equipment. However, in comparison, export restrictions on semiconductor materials hold greater potential, possibly gaining policy attention due to Huawei's involvement, thus becoming a key direction for the next round of U.S.-China competition.


Summary: Daishin Securities raises SK Hynix target to KRW 320,000, calling it the beginning of a new era; author shares/endorses bullish call.

SK Hynix: The Beginning of a New Era – Daishin Securities Raises Target Price to 320,000 Won https://t.co/eHwWP5KeeA


Morgan Stanley believes MediaTek's revenue certainty for the Google TPU project has strengthened, maintaining a target price of NT1,888. If MediaTek successfully secures the Meta MTIA ASIC order, it could potentially reach a bull-case scenario valuation of NT2,600

Summary: Morgan Stanley maintains Buy on MediaTek NT$1,888 PT; bull case NT$2,600 if Meta MTIA order secured alongside Google TPU ramp.

MediaTek: Benefiting from Google TPU Project Progress, Could Reach Bull Case Target Price if Meta Order Secured
Morgan Stanley (C. Chan, July 09, 2025)

Morgan Stanley believes MediaTek's revenue certainty for the Google TPU project has strengthened, maintaining a target price of NT1,888.If MediaTek successfully secures the Meta MTIA ASIC order,it could potentially reach a bull−case scenario valuation of NT2,600.

Google TPU Project Expected to Tape-Out Mid-September, Significant Revenue Contribution in 2026
MediaTek's involvement in Google's next-generation TPU is expected to complete tape-out by mid-September 2025. The primary revenue contribution is anticipated in 2026, projected to exceed $1 billion for the full year, with most of it concentrated in Q4 2026.

Meta MTIA Project Competition Fierce, MediaTek and Broadcom Are Final Candidates
In Meta's AI accelerator MTIA project, MediaTek and Broadcom are the two final design service candidates. Broadcom holds an advantage in complex packaging, while MediaTek is more attractive in terms of Total Cost of Ownership (TCO) due to its operational efficiency and cost control capabilities. A decision is expected by the end of July or early August, with the current winning probability at 50%.

Maintaining Optimistic Outlook for MediaTek's AI ASIC Business
Morgan Stanley considers the Meta MTIA project as part of its bull case scenario. The firm believes that if MediaTek wins orders from both Google and Meta, two major CSPs (Cloud Service Providers), it will significantly enhance its market position and revenue flexibility in the AI ASIC domain.

Smartphone Business Stable in Short Term, Q3 2025 Revenue Slightly Down
Although MediaTek previously canceled some wafer foundry orders, it has recently re-ordered, reflecting stable demand for smartphones in China. Q3 2025 revenue is expected to decrease by 8.5% QoQ, but the overall short-term fundamentals remain stable.


EPS and Target Price Upgraded, Positive on GenAI Cycle Driving Overall Growth… the target price is raised to $530

Summary: Morgan Stanley raises MSFT PT to $530 on Azure AI revenue upside, 33% CAGR to $200B+ by 2028, and mid-double-digit EPS CAGR from GenAI cycle.

Microsoft: Azure AI Business Accelerating ROI from GenAI Investments, Opening Up Valuation Upside

Morgan Stanley (K. Weiss, 2025/06/26)

Morgan Stanley believes that Microsoft's capital expenditures in the generative AI (GenAI) sector are yielding rapid returns. Both Azure AI's revenue and profitability have upside potential. Under more optimistic model assumptions, Azure revenue could exceed $200 billion by 2028.

Capex Derivation Model Shows Upside for Both Azure AI Revenue and Profitability

Morgan Stanley's capital expenditure derivation model, used to estimate Azure AI revenue, suggests that current model assumptions are conservative. The firm believes that Azure AI's gross margin has already surpassed 40% and is likely to reach or even exceed 50% in the future. In a 60% gross margin scenario, Azure revenue would be significantly higher than current expectations, implying higher returns per unit of capital expenditure.

Investor Views on OpenAI Dependence Diverge, But Growth Prospect Remains Attractive

Morgan Stanley notes that some investors believe OpenAI's contribution is underestimated and that Azure's growth sustainability is strong. Others, however, are concerned about Microsoft's reliance on OpenAI and the risks associated with changes in their relationship. In response, the firm emphasizes that Azure AI's core business itself exhibits a triple-digit growth trend, which is far from peaking.

Updated OpenAI Model Still Indicates Conservative Azure Forecasts

After incorporating new information and assumptions into the model, Morgan Stanley found that if OpenAI revenue is calculated solely based on current assumptions, the growth of the "other AI customers" (non-OpenAI) segment is underestimated in the model, even showing a negative growth trend. This clearly contradicts the reality of current triple-digit growth. If a more reasonable assumption of slowing growth is applied, overall Azure revenue could maintain a 33% compound annual growth rate (CAGR), exceeding $200 billion by 2028.

EPS and Target Price Upgraded, Positive on GenAI Cycle Driving Overall Growth

Morgan Stanley expects Microsoft to benefit from the structural shift in IT spending driven by GenAI and to achieve mid-double-digit EPS CAGR growth through cost control and business synergy. Based on a FY27 EPS of $17.71, the target price is raised to $530, corresponding to a 30x price-to-earnings (P/E) ratio, reflecting its dominant position and strong execution in the AI cycle.

$MSFT


Summary: Shares UBS bullish foundry note: TSMC favored on N2 ramp + mature node pricing recovery; Intel continues outsourcing to TSMC supporting demand.

Semiconductor Foundry: N2 Growth and Mature Nodes Advancing in Parallel, Capacity Expansion and Pricing Resilience Expected
UBS (S. Lin, 25/06/06)

Over the next three years, driven by multiple platform migrations including smartphones, PCs, and servers, the N2 node is expected to become TSMC’s second-largest process node. Concurrently, utilization and pricing improvements in mature processes will underpin the broader foundry industry.

• Robust N2 Node Demand Expansion
UBS’s TAM analysis suggests Qualcomm and MediaTek’s flagship SoCs will migrate concurrently with Apple’s A-series to N2, marking the first simultaneous generation upgrade. From H226E through 2027, AMD server platforms and Intel’s PC outsourcing will further drive N2 demand. Accelerated chip upgrades driven by AI applications for mobile and PC platforms, alongside future server expansions, are expected to result in approximately 200K wafers/month demand for N2 by 2028. Strong demand supports TSMC’s capacity expansion plans to 80K wafers/month by year-end 2026 and 140K wafers/month by year-end 2027, despite potential tariff-related impacts on iPhones.

• Intel 18A In-house Production and Continued Outsourcing
Intel plans to start volume production of 18A (Panther Lake) by late 2025, with an initial yield estimated around 50% and production reaching 20K–30K wafers/month by 2027. To maintain time-to-market advantages and supply flexibility, Intel will continue outsourcing PC CPUs, such as Nova Lake, to TSMC’s N3/N2 nodes, with potential partial outsourcing of Diamond Rapids server CPUs as well. Regarding its foundry service (18AP), final PDK availability may not materialize until Q1 2026, with external customers’ mass production likely to begin in H2 2028 at the earliest.

• Mature Process Utilization and Pricing Recovery
UBS believes the worst has passed for mature nodes, forecasting utilization to recover from 60%+ in 2024 to over 70% by mid-2025. China domestic 28/40nm utilization remains above 80%, averaging around 85% throughout the year. Driven by production line restarts and distributor restocking, utilization could see stability or slight improvement through Q3 2025, with a seasonal decline expected from Q4 2025 to early 2026. Pricing power for mature nodes is expected to improve in the second half, as leading manufacturers like Hua Hong have already started raising prices, reflecting a better supply-demand balance.

• Risks and Implications
Key points to watch include: TSMC’s N2 capacity expansion timeline and potential demand forecast deviation due to iPhone shipment fluctuations; the balance between Intel’s outsourcing and internal 18A production impacting foundry revenue structure; and potential oversupply resulting from increased Chinese WFE investments in the 28/40nm domain. Overall, driven by both N2 node expansion and mature process improvements, leading foundries are expected to continue benefiting from high utilization rates and pricing strength. UBS favors companies competitively positioned in both N2 expansions and mature process pricing recovery.

$TSM $INTC


Intel will continue outsourcing PC CPUs, such as Nova Lake, to TSMC's N3/N2 nodes, with potential partial outsourcing of Diamond Rapids server CPUs as well


analysts have now revised their forecast for TSMC's gross profit margin in the second half to approximately 55%… the 'dual ratios,' including gross profit margin and operating profit margin, may fall to the low end of the guidance, making it difficult to surpass the guidance targets… TSMC may not achieve the market's expectation of profit growth this quarter compared to Q1, at most only matching the previous quarter or even facing a single-digit percentage sequential decline

Summary: Bearish TSMC near-term: NTD 12% appreciation crushing margins ~5pp, H2 GPM revised down to ~55%, Q2 profit likely misses growth expectations vs Q1.

Report: TSMC Faces Two Headwinds, Q2 Profit Under Pressure; Analysts Forecast "Dual Ratios" May Dip to Low End of Guidance

TSMC (2330) has recently encountered two major external headwinds. Not only are there reports that the White House intends to revoke the waivers allowing its mainland China fabs to acquire U.S. technology, but as Q2 rapidly approaches its end, the New Taiwan Dollar's strong appreciation of 12% against the U.S. dollar this quarter appears irreversible. This severe appreciation is significantly impacting TSMC's gross profit margin and operating profit margin by nearly 5 percentage points each, severely eroding profits. The company's net profit for this quarter may fall below that of the previous quarter, failing to meet market expectations for continuous profit growth.

Furthermore, the exchange rate has a substantial impact on the gross profit margin of export-oriented TSMC, which is heavily reliant on U.S. dollar revenue. Analysts had initially expected TSMC's gross profit margin in the second half of the year to challenge the 58% range. However, due to exchange rate interference, analysts have now revised their forecast for TSMC's gross profit margin in the second half to approximately 55%. In addition, initial yield adjustments during the mass production of 2nm in the second half of the year may also dilute the gross profit margin.

Regarding the exchange rate issue, TSMC Chairman C.C. Wei stated directly at the June shareholders' meeting that exchange rate fluctuations would affect TSMC's gross profit margin and operating profit margin. He projected that for every 1% appreciation of the New Taiwan Dollar, the "dual ratios" would each be impacted by approximately 0.4 percentage points, calling it "a very significant number." TSMC can only continue to strive to maintain technological leadership and sell at "the value it deserves."

At its April investor conference, TSMC released its Q2 U.S. dollar revenue guidance of between $28.4 billion and $29.2 billion, with an average sequential increase of nearly 13%. These figures were higher than analysts' average sequential growth estimate of around 6% at the time. However, TSMC's average exchange rate assumption in April was NT$32.5 to US$1. With the New Taiwan Dollar appreciating more than 2% against the U.S. dollar in the past month and a strong 12% appreciation this quarter, closing at 29.529 last Friday (June 20), the average exchange rate has significantly diverged from TSMC's April investor conference assumption.

Based on C.C. Wei's previous statements regarding the impact of exchange rate fluctuations on TSMC's "dual ratios," the New Taiwan Dollar's strong 12% appreciation this quarter has had a significant impact, affecting both the gross profit margin and operating profit margin by nearly 5 percentage points each.

Considering that only six trading days remain in June, making it difficult to reverse the strong appreciation of the New Taiwan Dollar, analysts estimate that TSMC's Q2 U.S. dollar revenue guidance is still likely to be met. However, the "dual ratios," including gross profit margin and operating profit margin, may fall to the low end of the guidance, making it difficult to surpass the guidance targets. Based on this, TSMC may not achieve the market's expectation of profit growth this quarter compared to Q1, at most only matching the previous quarter or even facing a single-digit percentage sequential decline, though it will still be better than the same period last year, setting a new record for the same period in history, with estimated earnings per share of NT$13 for the quarter.

Data from the past eight years show that TSMC's Q2 profit has outperformed Q1 approximately 50% of the time. In Q2 2024, TSMC's profit surpassed Q1 due to the rapid growth of AI and HPC applications. However, in Q2 of 2023, 2021, 2018, and 2017, profits all declined compared to Q1.

Foreign media have reported that the U.S. may revoke the waivers enjoyed by major semiconductor manufacturers such as TSMC, Samsung, and SK Hynix for acquiring U.S. technology in their mainland China fabs. The impact of such policy changes on TSMC's orders in mainland China remains to be seen. TSMC is scheduled to hold an investor conference in July to announce its Q1 financial report and operational outlook. With these two major headwinds emerging recently, the views of the company's senior management are garnering significant attention.

$TSM

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https://t.co/YFlRixS1TW


Summary: Shares GS Buy on Wistron (AI server ramp) and GS Neutral-to-positive on Inventec (stable margins, AI baseboard); adopting directional stances.

Wistron/Inventec: Wistron's Strong Growth, Inventec's Moderate Differentiation
Goldman Sachs (V. Jeng, 2025/06/08)

Goldman Sachs believes that Wistron benefited from the ASIC AI server and general-purpose server new product cycles, with May revenue increasing by 56% month-on-month. In contrast, Inventec experienced a 1% month-on-month decline due to exchange rate and product mix impacts. Both companies will face high base challenges in Q3, with Wistron showing stronger growth momentum in the AI server domain.

Wistron's Revenue and Shipments Soar

Goldman Sachs points out that Wistron's May revenue was NT$208.4 billion, a month-on-month increase of 56% and a year-on-year increase of 162%, exceeding estimates by 11%. This growth primarily stemmed from: 1) Increased penetration of Wiwynn ASIC AI servers, driving its revenue up by 187% month-on-month; 2) Wistron's own rack-level AI server shipments reaching approximately 800–900 units in May (compared to only 100–200 units in March–April); 3) The new product cycle for general-purpose servers; 4) PC replacement and AI notebooks driving NB shipments up by 6% and desktop shipments up by 38%. Goldman Sachs maintains a cautious stance on rack-level AI servers but is optimistic about the flexibility and superior gross margins of ASIC solutions.

Inventec's Revenue and AI Strategy Balance

Goldman Sachs estimates that Inventec's May revenue decreased by 1% month-on-month, mainly due to the appreciation of the NT dollar, limited exposure to rack-level AI servers, and a slowdown in PC and general-purpose server pull-ins in Q2. Looking ahead to Q3 2025, a high base, exchange rates, and earlier pull-ins are expected to lead to an approximately 8% month-on-month decline. Nevertheless, Inventec possesses cost and gross margin advantages in the H200/B200 baseboard-level AI server domain. With its comprehensive AI server product line covering both GPU and ASIC engines, and extensive coverage of US and Chinese CSP customers, Goldman Sachs holds a neutral-to-positive view on its long-term performance.

AI Server Business Comparison and Strategy
Goldman Sachs emphasizes that Wistron, leveraging Wiwynn's deep expertise in ASIC AI servers, has expanded from the component level to the system level, enabling it to quickly capture demand from hyperscale cloud service providers. Inventec, on the other hand, with its low budget threshold and high gross margin characteristics for baseboard-level solutions, possesses differentiated competitiveness in specialized AI scenarios. Both companies are actively deploying AI servers, but Wistron's system delivery and shipment pace are clearly faster, leading to more significant short-term growth.

Ratings and Outlook
Goldman Sachs maintains its "Buy" rating for Wistron, citing the rapid ramp-up of its AI server business and better-than-expected overall revenue. It maintains a "Neutral" rating for Inventec due to its lower revenue base, relatively controllable month-on-month fluctuation risk in Q3, and its ability to achieve stable gross margins and cash flow through AI server baseboard solutions.

$NVDA


Goldman Sachs holds a neutral-to-positive view on its long-term performance

Inventec Corporation 2356.TW Taiwan server ODM


Morgan Stanley raises NVIDIA's target price from $160 to $170

Summary: Morgan Stanley raises NVDA PT from $160 to $170; author shares/adopts bullish stance.

Morgan Stanley raises NVIDIA’s target price from $160 to $170. https://t.co/v1H3XCeChg


Summary: MTIA v2 mass production by year-end: bullish META on EPS improvement, bullish Broadcom and Celestica as ASIC/server beneficiaries.

industry='Internet Content & Information'

Meta Completes Development of Its Own Chip, MTIA v2 - By BK Tech Insight

Meta's Self-Developed Chip (MTIA v2)
- Meta's inference chip test has been successfully completed.
- Meta plans to begin mass production of its self-developed AI chip, MTIA v2, by the end of this year.
- Broadcom, a U.S. company, is primarily responsible for the chip's design.

Technical Features
- MTIA v2 is for AI inference and accelerating ad recommendation algorithms.
- It uses an 8x8 PE (Processing Element) structure internally.
- Adopts TSMC's 5nm process, with significantly increased SRAM capacity and LPDDR5 memory.
- Maximizes efficiency by incorporating a large amount of SRAM instead of costly HBM.

Implications
- Meta's completion of MTIA v2 development is very important and has significant implications.

1. Expansion of SRAM-centric Inference Chips Without HBM

- Tesla and Grok have successfully developed and are operating HBM-less SRAM-centric inference chips, but Grok's low market share has kept them from gaining much attention.
- However, the largest AI workload currently is Meta's advertising/recommendation algorithms.
- Therefore, Meta's shift from GPU-based inference workloads to its own SRAM-based ASICs is highly significant.
- It is crucial to closely observe whether SRAM-based ASICs can successfully establish themselves.
- Attention to the ASIC and ASIC server value chains is warranted.

2. Re-evaluating Meta's Internal Margin Improvement

- Meta's cost of revenue for advertising/recommendation algorithms is essentially the GPU price and GPU operating costs.
- If Meta successfully transitions to lower-cost SRAM-based ASICs, Meta's own EPS is highly likely to improve.
- The estimated cost of Meta's inference chip is around $5,000, significantly lower than general GPU prices. However, for Meta's advertising/recommendation algorithm inference, its performance is not significantly inferior, suggesting excellent cost-effectiveness.

Conclusion
- It is estimated that Meta, with the single largest AI workload, has successfully tested its SRAM-based ASIC.
- Mass production is expected to begin by the end of the year. If the transition is successful, the beneficiaries in the semiconductor sector are likely to change.
- Furthermore, if the proliferation of inexpensive inference ASICs succeeds, Broadcom and Celestica (a big tech ASIC server assembly company) are expected to benefit.
- The EPS improvement of big tech companies and solution providers transitioning to inference chips should also be followed up on.


Broadcom and Celestica (a big tech ASIC server assembly company) are expected to benefit


Summary: Goldman Sachs Q3 2025 Top Ideas: Buy META, GOOGL, V, WMT, ETN, CMI; Sell AAPL, F, LUV, HSY.

Goldman Sachs Q3 2025 Top Ideas

Buy:
Meta
Google
Visa
Walmart
Eaton
CMI

Sell:
Apple
Ford
Southwest
Hershey https://t.co/zdghjL1MSv


Upgraded to "Hold" given stronger-than-expected memory price increases in CY3Q25 and rapid yield improvements for Micron's HBM3e-12Hi

Summary: Shares GF Securities upgrade of MU to Hold; near-term beat on pricing/HBM yields but cautious from 4Q25 on China competition and cycle risk.

[GF Securities Overseas Electronics and Communications] Micron (MU US): Upgraded to "Hold" on Price Increase and Rapid HBM Yield Improvement

Upgraded to "Hold": We upgraded Micron to "Hold" given stronger-than-expected memory price increases in CY3Q25 and rapid yield improvements for Micron's HBM3e-12Hi. Our target price is based on FY25E 2.5x P/B. Despite short-term strength, we remain cautious about the memory cycle starting from CY4Q25 due to rising influence from Chinese memory suppliers and sluggish end-market demand. On the other hand, memory manufacturers will achieve revenue growth for 10 consecutive quarters through CY3Q, marking the longest upcycle. Considering Micron's elevated valuation amid the AI-driven boom, we see an unfavorable risk-reward profile.

Strong Earnings Expectations: We forecast better-than-expected memory price increases for the third fiscal quarter, driven by restocking in PCs/servers and AI demand. We predict the contract average selling price (ASP) of non-HBM DRAM to rise by 7% QoQ (previously expected 4%), with server DDR5 prices up by 8–10%. Hence, we estimate Micron’s FY3Q25 revenue to reach USD 9.2 billion, exceeding the upper end of guidance (USD 9 billion). For FY4Q25, we expect revenue to reach USD 10.6 billion (above consensus of USD 9.8 billion). Profit margins are also anticipated to beat expectations, benefiting from favorable pricing and increased HBM revenue share.

Rapid HBM Yield Improvements; Samsung Faces Further Setbacks: Micron significantly improved its HBM yields using Hanmi’s TCB tool in CY2Q25, achieving a 70% yield for HBM3e-12Hi and 75% yield for HBM3e-8Hi. With increased market share among NVIDIA, AWS, and AMD, we raise our 2025 HBM revenue forecast for Micron to USD 7.3 billion, above consensus expectations of USD 6.0–7.0 billion. Conversely, Samsung failed to pass its third round of HBM certification in June, with the next round expected in September. We expect HBM pricing to remain stable in 2026.

Slowing Price Momentum and Rising Competition from China in 4Q: We expect price momentum in CY4Q25 to slow significantly due to weak end-market demand and potential PC memory destocking. Additionally, Chinese memory manufacturers' influence on the DDR5 market will significantly increase in 2026. ChangXin Memory (CXMT) is expected to completely exit DDR4 and shift to DDR5 by 1H26, increasing its DDR5 bit shipment share from 2–3% in 2025 to 8–9% in 2026, thereby exerting significant downward pressure on DDR5 prices.

$MU

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Source: https://t.co/OCPbVURCrN


Summary: Bullish MU: post-earnings dip driven by unfounded concerns over blended ASP mix shift and 2026 HBM lock-in timing, not fundamental weakness.

Why did Micron's stock price turn downward despite strong earnings? - Korea Daishin Securities

If there's market concern, I believe it's because Micron's stock price turned downward after its earnings announcement. I judge that the stock price's downward turn stemmed from three main factors.

1. Decline in DRAM Blended ASP

While DRAM sales significantly exceeded expectations, the Blended ASP (Average Selling Price) slightly decreased from the previous quarter (ASP: estimated to decline 1-2% QoQ). The market seems to be questioning this ASP decline, despite HBM revenue, which has a higher ASP than general-purpose products, increasing by 50%.However, I don't believe the decline in Blended ASP was due to weakness in the ASP of leading-edge products like HBM and high-value DDR5. It is estimated that leading-edge products continue to see strong ASPs amidst a tight supply-demand environment. I judge that the Blended ASP decline this quarter was largely due to changes in product mix (accelerated sales growth of mobile and industrial products, which have lower ASPs compared to servers).

2. When will the 2026 HBM volume lock-in occur?

I believe that in this earnings announcement, Micron Technology provided sufficient outlook on the direction of HBM demand growth, related technology and product competitiveness, and market share expansion. However, there was no mention of the 2026 Nvidia-bound volume lock-in, which the market is focused on, and I judge that this partly served as a catalyst for profit-taking.I believe it would have been difficult to materialize the 2026 volume lock-in in this earnings announcement. I judge that the volume lock-in will become concrete after priority negotiations with leading companies are completed. As HBM4, a new product for 2026, is a leading-edge product that can contribute to AI technology innovation, related demand will be sufficiently strong, and I believe that market expectations can be met during this process. If so, I judge that the related concerns are unfounded.

$MU


Summary: Citi impressed by NVIDIA's full-stack AI moat; networking business up 64% sequentially to $5B with broad-based growth across scale-out and scale-up.

Citi’s Take on NVIDIA

Yesterday, during Citi's annual Silicon Valley bus tour, we held an investor meeting with Kevin Deierling, SVP of NVIDIA's Networking Business. Overall, we were impressed by NVIDIA's "full-stack moat" in AI, built across computing, networking, and storage. Here are the key takeaways:

Everything Revolves Around AI

The company focuses on the networking functions required for AI. NVIDIA has a unified network operating system, focused on optimizing compute and storage, with the core goal of achieving the lowest user token cost per second (TCO). Dynamo is the operating system for AI factories. Since Mellanox was acquired by NVIDIA, the team stopped developing campus switches and shifted focus to AI switches, because the response logic for telecom/large-scale web differs from that of AI agent flows.

Spectrum-X Sales

In the recently announced Q1 earnings, the networking business achieved robust sequential growth of 64%, reaching $5 billion. This growth appears to be broad-based, covering products from "scale out" to "scale up." On the "scale up" front, the company achieved over $1 billion in sales with its NVLink solution; on the "scale out" front, the company continues to make progress with its Spectrum-X (Ethernet) product portfolio, adding two new customers. Spectrum-X's quarterly revenue run rate is $2 billion, encompassing both NICs (Network Interface Cards) and switches.

Infiniband vs. Ethernet

Ethernet is still evolving, but Infiniband remains the de facto standard. Hyperscale customers request Ethernet due to familiarity, and NVIDIA has strong capabilities in Spectrum-X. Although general vendors like Arista with its EOS system are trying to transition from traditional data center switching business to AI applications, NVIDIA's advantage lies in its complete technology stack. Broadcom's main product in the market is Tomahawk, not the Jericho platform, which offers programmability but adds latency.

NVLink

NVLink is an in-rack "scale-up" platform custom-built for AI. It connects multiple GPUs (up to 72) through a cache-coherent interface. Larger LLM models benefit from massive GPU scale-up, and sometimes developers build models based on the hardware GPU stack. Customers don't need to buy the entire technology stack from NVIDIA; they can purchase as needed, such as SuperNICs, switches, and NVLink Fusion for semi-custom AI infrastructure.

Co-Packaged Optics (CPO)

NVLink uses over three miles of copper cables. Copper wires have low power consumption and are inexpensive. However, the reason for using CPO when scaling out is that 10 to 15 inches of copper wire on a PCB can generate significant noise, requiring additional DSPs and retimers to handle it. To avoid this issue, optical components can be directly packaged next to the ASIC, converting signals into optical signals, thus eliminating the need for DSPs and retimers.

$NVDA


Summary: Korea Investment & Securities reiterates Buy on Samsung/SK Hynix and Overweight on semis; VEU revocation unlikely to be implemented, short-term selloff overdone.

Don't always respond to US fear marketing - Korea Investment & Securities

Samsung Electronics, SK Hynix Reportedly Reviewing VEU Revocation for Chinese Fabs

According to a recent Wall Street Journal report, the U.S. government is reportedly considering revoking Validated End-User (VEU) authorizations previously granted to Samsung Electronics, SK Hynix, and TSMC for their semiconductor fabs in China. VEU is a system that allows the supply of U.S. equipment and technology without separate export licenses. If VEU is revoked, Samsung Electronics and SK Hynix would need to obtain individual approvals from the U.S. Department of Commerce for each piece of U.S. equipment brought into their Chinese factories.

Low Probability of Actual Implementation Considering Supply Chain Impact

Similar to semiconductor reciprocal tariffs, from a supply chain perspective, we believe the actual implementation of this measure is unlikely. Accordingly, we assess that the impact on domestic semiconductor-related companies will be limited. This is because if the introduction of equipment and technology to Samsung Electronics and SK Hynix's fabs is blocked, it would disrupt the global memory supply chain and cause memory prices to rise, ultimately harming major U.S. customers through increased component procurement costs. Samsung Electronics' Xi'an fab accounts for approximately 40% of its NAND capacity, while SK Hynix's Wuxi fab accounts for 40% of its DRAM capacity, and its Dalian fab accounts for 30% of its NAND capacity.

VEU Revocation Likely a Strategic Pressure Tactic by the U.S.

The mention of possible VEU revocation can be interpreted as a strategic pressure tactic by the U.S., rather than an immediate restrictive measure. There is also significant opposition within the U.S. government. According to the Wall Street Journal report, opponents within the administration are concerned that "revoking VEU could actually help Chinese companies grow and strengthen the Chinese government's control over those factories."

Short-Term Stock Price Adjustment is Excessive, Mid-to-Long Term Memory Investment Momentum Remains Valid

Following this report over the weekend in Korean time, stock prices fell, mainly for U.S. technology and semiconductor equipment stocks. Today, in the Korean market, stock prices are declining, primarily for semiconductor front-end equipment stocks. However, we believe this stock price adjustment is excessive. While intensified U.S. sanctions against China and China's efforts to increase semiconductor self-sufficiency are mid-to-long term risk factors, the likelihood of immediate damage to the operation and investment of Samsung Electronics and SK Hynix's Chinese factories in the short term is limited. We believe that an excessive stock price decline due to short-term policy noise is likely to see a rebound, and we maintain our "buy" and "overweight" opinion on the semiconductor sector.


an excessive stock price decline due to short-term policy noise is likely to see a rebound, and we maintain our "buy" and "overweight" opinion on the semiconductor sector

Korea semiconductor sector proxied by EWY ETF


US Firms Reiterate Positive Outlook... revenue contribution is expected to continue until at least 2028. Brokers estimate that TPU will contribute $1 billion to MediaTek's revenue next year, doubling to $2-3 billion the year after. The positive rating remains unchanged.

Summary: Shares/endorses US banks' reiterated positive on MediaTek; TPU revenue $1B→$2-3B, 2nm orders potentially through 2028.

2454 MediaTek: US Firms Reiterate Positive Outlook
Major US investment banks believe the market is overly concerned about a US client's TPU (Tensor Processing Unit) progress. Brokerage channel checks indicate that the TPU compute die tape-out is expected in mid-August, not late September as market rumors suggest. Furthermore, the I/O die has already taped out. The client will use this for training, specifically the v8p version, not the v7e as rumored in the market.

It's also believed that MediaTek will secure 2nm TPU orders (potentially named v9). While it's uncertain if this will be an exclusive supply, the revenue contribution is expected to continue until at least 2028. Brokers estimate that TPU will contribute $1 billion to MediaTek's revenue next year, doubling to $2-3 billion the year after. The positive rating remains unchanged.

$AVGO

早安！6/16 外電綜合整理

- 2454聯發科: 美系重申正向
美系大行認為市場過度擔憂美系客戶TPU 進度，券商通路訪查認為TPU compute die 的tape out時間落在8月中，而非市場傳言的9月底，而且I/O die 已經taped out 了。客戶會用在訓練，而且是v8p, 而非市場傳的v7e, 接下來認為聯發科也會取得2nm


Summary: Relays UBS note flagging Taiwan ADP export beat as positive signal for NVDA Q2 data center upside; TSMC sales cited as next verify.

Taiwan Export Data: June ADP Exports Show Significant Increase, Potentially Indicating Upside for NVDA Q2 Data Center Revenue
UBS (T. Arcuri, July 08, 2025)

UBS notes that despite fluctuations in the correlation between Taiwan's ADP data and NVIDIA's revenue, June's better-than-expected export performance initially suggests that AI server demand may remain strong.

June ADP Exports Significantly Exceed Seasonal Average
Taiwan's Ministry of Finance announced that June exports of automatic data processing (ADP) equipment (excluding notebooks) reached $14.18 billion, a 7.5% month-over-month increase. This is significantly higher than the seasonal average of -4.5%, making it the second strongest June performance in over a decade, second only to the historical high.

FQ2 Overall Export Projections Far Exceed Current NVDA Revenue Expectations
If July's proportion of FQ2 exports is historically consistent, UBS estimates that total ADP exports for this quarter will reach $44 billion, a 24% quarter-over-quarter increase. This would mark a new high after FQ1's 63% growth, far exceeding UBS's forecast of only a 1.5% quarter-over-quarter growth for NVDA's data center revenue.

Current Data, Though Affected by Tariffs, Still Holds Positive Reference Value
While some of the export growth may be driven by accelerated shipments due to US tariff policies, and the correlation between ADP and NVDA's data center revenue has been unstable over the past 8 to 10 quarters, UBS believes this data can still be regarded as a positive signal in the short term.

ADP Data Coverage Differs from NVDA's Actual Revenue Structure
ADP export statistics only cover complete servers and do not include components like AI chip exports assembled in China or Malaysia, nor do they include networking equipment. Additionally, they comprise traditional products from non-NVDA manufacturers, leading to structural differences compared to NVDA's revenue.

Future Focus on TSMC Sales to Verify Supply/Demand Strength
UBS advises investors to closely monitor TSMC's June sales data to further verify whether this surge in ADP exports is due to a structural increase in AI demand or merely a short-term accelerated shipment behavior. This will help provide a clearer judgment of NVDA's fundamentals.


UBS advises investors to closely monitor TSMC's June sales data to further verify whether this surge in ADP exports is due to a structural increase in AI demand


we expect Nvidia rack revenue for 2026 to more than double YoY, driven by both units (off a low base) and ASP (from mainly NVL36 to all NVL72)

Summary: Shares/endorses Macquarie bullish NVDA call: 2025 GB rack shipments 10k racks, 2026 rack revenue more than doubling YoY on units and ASP uplift.

Macquarie Securities: With no major transition gap between GB200 and GB300 in late-3Q to early-4Q, we expect 2025 GB rack shipments of 10k (or 6.5k NVL72 equivalent racks), contributing 25-30% of total revenue. We also expect Nvidia rack revenue for 2026 to more than double YoY, driven by both units (off a low base) and ASP (from mainly NVL36 to all NVL72).

$NVDA


Apple's very strengths might, ironically, be hindering its fundamental transformation towards AI innovation, creating a crisis situation that warrants a serious internal review... the iPhone could ultimately become a device like a BlackBerry—beautiful, but with a nonsensical Siri that responds as slowly as a flickering fluorescent light

Summary: Bearish AAPL: ecosystem lock-in blocks AI progress, privacy policy limits data use, M-chips poorly suited for AI; risks iPhone becoming irrelevant like BlackBerry over next 5–10 years.

Apple WWDC25 Commentary by Professor Seokjun Kwon of Sungkyunkwan University, South Korea

At WWDC on June 9, contrary to many people's expectations, Apple showed that there was nothing new that could truly be called an advancement in Apple's unique AI. As one of the leading IT giants of our time, Apple naturally invested in AI at a corporate level, and in 2024, they even announced an AI service called 'Apple Intelligence.' Therefore, many investors had anticipated the disclosure of an AI at a comparable level in the rapidly changing global AI innovation environment. However, reports from domestic and international media suggest that when the lid was finally opened, the situation was such that one might wonder if Apple had, in fact, given up on AI.

It's not that Apple lacks capital, personnel, or an ecosystem. If there's a reason for their significant lag in AI innovation compared to other companies, it's paradoxically due to the characteristics of their robust ecosystem. In particular, the device ecosystem, spearheaded by the iPhone (+iPad) and MacBook, serves as a technological moat that allows Apple to outpace competitors. Yet, it simultaneously acts as a moat that works against their progression towards AI and AGI.

Apple will likely continue to upgrade the iPhone series, and to do so, they will invest enormous capital to produce Apple Silicon, specifically the M-series chips, using TSMC's most advanced processes, such as 2-nanometer, 18A, and 14A, which are the most expensive foundry and packaging processes of their time. However, at least the M-series chips are not suitable architectures for current AI model computations. Furthermore, Apple's blind spot in advancing AI innovation stems from the limitations in utilizing user information due to its device-centric ecosystem.

My understanding of Apple's basic policy is to excessively prioritize user privacy for the competitiveness of its device ecosystem. This policy will fundamentally provide users with a sense of security. Indeed, most of the features of Apple Intelligence, which Apple announced last year, run only on the device, and cloud-based AI is limited to a private cloud compute approach. However, precisely this policy makes real-time, large-scale LLM integration, which relies on cloud resources, almost impossible. Apple explicitly states externally that it does not use user personal data for foundation model training. While this might be an excellent approach for managing Apple's loyal customers, it severely reduces the inherent usability of the data embedded in Apple's touted ecosystem.

The M-series chips and AI are also not a good match. While they are undeniably optimized hardware for individual devices, they are fundamentally designed for mobile and standalone devices, revealing limitations in memory capacity and bandwidth for large-scale data parallel-high-speed processing. This is because they were not originally optimized architectures designed for large-scale matrix data. For instance, the M4 Pro includes a 20-core GPU, showing a computational power of about 9 TFLOPS in FP32 operations. In terms of computational power alone, it's excellent. However, even the outdated Nvidia A100 GPU showed 312 TFLOPS in FP16 operations and was already allocating nearly 1 TB/s of bandwidth with its HBM2, so in terms of overcoming the memory barrier, there's no comparison. Also, as far as I know, M-series chips do not natively support open-source frameworks like PyTorch, which leads to the cumbersome process of repeated compilation and conversion to run models on M-series chips. Apple might develop solutions to optimize so-called on-device AI on M-series devices. However, unless the aforementioned memory capacity and bandwidth issues are fundamentally resolved at the hardware level, there will be limitations on the size of data that can be processed on-device. This, in turn, acts as a fundamental limit to on-device inference performance.

The realistic approach Apple could take to overcome this would naturally be to adopt third-party models like Meta Llama, Google Gemini, or Anthropic's Claude and adapt them to their device environment. Apple has not shied away from attempting this. Siri is based on ChatGPT, and iOS utilizes Google Gemini resources. However, this is technically difficult due to user data privacy barriers and also challenging from a policy standpoint. It's not that Apple lacks technology that could circumvent this. For example, they could establish a separate channel for user-selected anonymized telemetry collection to control data collection. Also, for cloud integration, only embedding-level generation tasks could be performed on the device, while large-scale inference logic would be processed in an encrypted form in the cloud. Furthermore, well-known methodologies like distillation, quantization, and pruning could be attempted for computational optimization tailored to the M-series architecture.

From a hardware perspective, a more fundamental approach would be to develop AI-specific server chips. Apple's hardware engineers, with their accumulated experience in optimizing the M-series, would likely be capable of such work. The problem, however, lies in finding HBM suitable for the M-series. On the surface, it might seem like Apple could do as Nvidia, TSMC, and SK Hynix have done in forming an alliance to find sufficiently good GPU+HBM+logic die packaging combinations. But technically, this is by no means an easy task. For instance, Apple's M-chips use TSMC's InFO (integrated Fan-Out) packaging technology, where LPDDR memory is matched, and integration proceeds in SiP form. HBM, however, does not use the SiP method; it employs TSV-based silicon interposers from the outset, with bandwidth expansion and latency minimization in mind, leading to entirely different packaging. This requires TSMC's 2.5D packaging solutions like CoWoS and SoW-X. Since Apple did not design the M-series chips with these fundamental differences in packaging stages in mind, it's difficult to directly leverage SK Hynix's or TSMC's packaging expertise. If they were to utilize it, there's a method of separating the SoC and HBM into chiplets to reduce the interposer area and simultaneously improve yield and cost. However, in this case, Apple would need to negotiate with TSMC to completely convert one fab line for Apple's exclusive use. This means not just bearing the fab costs, but also investing in the construction of a new fab itself. Whether TSMC would accept such an investment is a separate matter.

There are further anticipated difficulties in Apple's process of utilizing M-chips as AI accelerators. Due to the large chip die area, even with maximum utilization of the interposer process, it's difficult to avoid issues of reduced manufacturing yield, which inevitably leads to increased costs. Furthermore, the CoWoS packaging, optimized for Nvidia dies, would need various modifications. Additionally, M-chips are matched with LPDDR4X/5, not HBM, so the memory controller would need to be redesigned to match with HBM. For example, HBM3E is based on a 1024-bit interface and supports up to 1.18 TB/s, and HBM4 is expected to be enhanced to 1.638 TB/s, while the M1 Max is 512-bit based with a bandwidth of only 400 GB/s. If they were to reuse the current matching method, they would either have to forcibly adjust HBM interface timings to match LPDDR4/5, or optimize by separating memory chiplets and logic chiplets. The problem with this approach is that it makes it difficult to fully leverage the original advantages of HBM.

While there are technically feasible solutions, it's hard to guarantee that any single direction currently offers a definitive answer. As stated repeatedly, Apple's unique ecosystem, especially the technical ecosystem barrier where user information is tied to devices, is now acting as an internal barrier for Apple as it expands into AI and opens up its ecosystem.

It might be a coincidence, but as if to offer an excuse for this, a few days before WWDC, Apple released a paper online titled 'The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.'
**https://t.co/LNQ1jpTlu0
The main content of this paper analyzes the limitations of large reasoning models (LRMs) and reasoning (thinking) token usage patterns, utilizing several well-known puzzles to test the performance of publicly available LRMs. Examples include Towers of Hanoi, Checker Jump, River Crossing, and Block World. Apple's researchers also showed test results indicating that the correlation between AI reasoning models, particularly the intelligence index derived as a function of the number of parameters, does not necessarily scale proportionally, contrary to popular belief. Apple's researchers claimed to have observed that AI reasoning models, despite their immense size, tend to copy and memorize similar patterns from existing training data rather than truly understanding the problems, and then produce results. This might imply that AI reasoning models, contrary to what is known, do not necessarily function by effectively utilizing a massive parameter space to solve more complex problems more accurately. A particularly interesting phenomenon observed was that these reasoning models experience a kind of 'critical phenomenon.' That is, beyond a certain apparent threshold, they exhibit paradoxical behavior, seemingly reducing their reasoning effort. This suggests that it's not true reasoning but rather a way of avoiding problems by combining plausible patterns from already learned data within the limits of computational and energy resources.

If we take the claims and observations in this paper at face value, Apple's AI researchers are, at the very least, sending a warning message that the development of massive reasoning models, which Meta, Google, OpenAI, and DeepMind are competitively developing and pouring huge capital into daily, will not directly lead to AGI. What's interesting is that this message came out a few days before WWDC (submitted June 7, 2025). While one should certainly be wary of over-interpreting coincidences, it makes one suspect that behind Apple's WWDC presentation, which barely touched on artificial intelligence, i.e., Apple Intelligence, and primarily focused on what seemed to be only incremental upgrades (I didn't even expect them to include naming conventions aligned with the year), there might be a case of Apple's "sour grapes" phenomenon regarding AI.
In other words, from Apple's perspective, they might have wanted to exclaim:
'We know we seem to be lagging in AI innovation on the surface. Investors, loyal Apple customers, don't worry! Of course, we have it all figured out! And it's not that we don't know how to use that massive personal information; we're just trying our best not to touch our loyal customers' data because it's precious! And AI reasoning might not be as grand as you think! AGI is a distant dream, and it turns out that scaling law was an illusion after all? We will approach AI more cautiously, more efficiently, and by spending money properly, and we will eventually achieve a safe, beautiful Apple Intelligence with a strong, unique Apple identity! So just wait a little longer!'
(Of course, this is just my speculation.)
In fact, this Apple paper has various points of criticism scattered throughout, which supports the idea that this hunch might not just be mere speculation. For example, the mathematical puzzles tested by Apple's researchers are somewhat disconnected from real-world, multimodal reasoning environment performance tests. This is because such puzzles are often used to test the efficiency of computations or the scalability of algorithms. The reason these types of puzzles are frequently used for testing algorithm efficiency and scaling characteristics is that they have relatively well-defined rules and state spaces, and algorithms can also be regularized. If Apple had intended to point out the limitations of existing LRMs, it would have been better if they had added various benchmarks to these puzzle tests and also demonstrated results by specifying a few more hybrid tasks leading from puzzles to coding and mathematical problem-solving. There's another aspect of Apple's researchers' paper that can be criticized. One of the core arguments of this paper is that 'even if the token resources required for reasoning are increased, if the complexity exceeds a certain level (i.e., passes a threshold), the model's reasoning ability stagnates or even decreases.' If misinterpreted, this could lead to the conclusion that 'the scaling law of reasoning models is an illusion!' However, this directly contradicts observations from actual language model scaling laws. Hundreds of scaling-related studies to date have consistently shown that performance improves in a power-law manner as the number of parameters increases, and beyond a certain size, performance is observed to move towards saturation. At the very least, performance might reach saturation, but it doesn't decrease. Indeed, as Apple's researchers showed in their paper, there is no evidence of scaling experiments for reasoning computational power in this paper. This might be because Apple does not have a GPU-based AIDC large enough to test a parameter space big enough to confirm scaling trends. (That is, Apple might not have hundreds of thousands of Nvidia GPUs like Microsoft, Google, or Meta.) If they had intended to conduct scaling tests, it would have been better to compare various models like ChatGPT's o1, o3, etc., in the same puzzle environment. Verifying the scaling law is similar to verifying the scaling law of large language models, and for this, Apple's researchers should have tested combinations of training data, parameters, and computational load and shown the performance curve. Also, if they intended to test saturation for the size of the parameter space, it would have been better to fix the number of parameters and test performance degradation by varying the degree of fine-tuning. The Tower of Hanoi problem tested by Apple's researchers is a case in point. This is a classic problem with a precisely known algorithm, and Apple's researchers explicitly provided it to the computer, yet they reported no performance improvement. However, in that process, there was no analysis of internal token allocation, attention patterns, or memory resource usage patterns. In other words, it is unclear how the explicit algorithm affected the reallocation of internal computational resources.

Given Apple's accumulated innovation know-how, its robust ecosystem, and the still powerful optimization technology of its M-chips, it is premature to easily conclude that Apple will fall behind in AI innovation based on a few signals. However, it's worth considering whether Apple's very strengths might, ironically, be hindering its fundamental transformation towards AI innovation, creating a crisis situation that warrants a serious internal review. Apple will continue to invest heavily in beautiful designs and devices with an inviting aura, but that will likely come after the current AI innovation frenzy has matured sufficiently. One thing is certain: Apple cannot afford to sit back and merely observe this maturation from the sidelines while other competitors invest heavily in AI. The smartphone platform, including the iPhone, has been around for almost 20 years, and given the technology replacement cycle, the next 10 years, or even the next 5 years, are uncertain. When that time comes, a company that continues to focus solely on beautiful and aura-filled devices will likely fall behind in the competition.

Apple's strong cash reserves, hardware engineers, and technological ecosystem can still be utilized as unique assets. However, for them to truly become helpful assets, Apple might need to transform into an entirely different identity than it is now. If they miss that timing, the iPhone could ultimately become a device like a BlackBerry—beautiful, but with a nonsensical Siri that responds as slowly as a flickering fluorescent light.

$AAPL

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https://t.co/t8wWUy9nZ5


Maintain Overweight investment opinion. Raise target price from KRW 285,000 to KRW 360,000.

Summary: JPM reiterates Overweight on SK Hynix, raises PT to KRW 360K on HBM leadership, 4-year earnings upcycle, and AI-driven ASP expansion.

SK Hynix: Target Price Raised to KRW 360,000 (JPM)

1. Investment Opinion and Target Price

- Maintain Overweight investment opinion.
- Raise target price from KRW 285,000 to KRW 360,000.

2. Continued Leadership in HBM Market

- Proactive response compared to competitors (pre-emptive supply of HBM4 samples to NVIDIA in March).
- Maintain technological lead through exclusive material contracts.
- Expected expansion of HBM4 market share due to competitors' certification delays (expected 37% production share by 2026).

3. Structural ASP Increase Driven by Expanding HBM Revenue Share

- HBM revenue share expected to exceed 50% after 2026.
- HBM4 ASP has a 35% premium structure compared to HBM3E.
- Transition to a highly profitable structure centered on HBM, anticipating an extraordinary earnings upcycle for the next four years.

4. Strengthening Link with AI Ecosystem

- Enhance HBM4/4E performance competitiveness through logic die collaboration with TSMC.
- Expected to benefit in conjunction with ASIC market growth.

5. Economics of HBM Business

- HBM wafer cost at ~$22,000, approximately 3 times that of DRAM.
- Investment of 10,000 wafers/month can generate $2.6 billion in annual revenue.
- OPM (Operating Profit Margin) is expected to remain above 60%.

6. Earnings Upcycle from 2024 to 2027

- Operating profit projected to increase for four consecutive years from 2025-2027 (recovering after 2023 deficit).
- 2025E EPS: KRW 43,005 → 2026E: KRW 47,632 → 2027E: KRW 63,591.
- ROE: 2025E 35.2%, 2026E 28.7%, 2027E 28.9%.

7. DRAM ASP Increase and Share Expansion

- DRAM ASP expected to continue increasing from 2025-2026 (2025E $4.4 → 2026E $5.0 → 2027E $5.9).
- DRAM revenue accounts for 75-84% of total revenue, contributing 96-97% to operating profit.

8. Limited Recovery Speed in NAND Segment

- Profitability limited by intensified competition and delayed eSSD price recovery.
- NAND operating margin expected to remain in the single digits, with structural improvement taking time.

9. Q2 2025 Earnings Preview

- Revenue: KRW 19.9 trillion, Operating Profit: KRW 9.0 trillion (OPM 45%).
- DRAM ASP expected to increase 2% QoQ, while NAND remains weak.
- Overall earnings expected to meet consensus.

10. Rerating Justification

- Enhanced earnings visibility based on HBM-centric structural profitability improvement and predictable AI demand.
- Expectation of shortened memory cycles and a shift to an AI demand-driven ASP cycle.
- Application of a premium based on HBM technology (P/B 1.9x, applying upcycle valuation).

11. Financial Stability and Cash Flow

- FCFF (Free Cash Flow to Firm) projected at KRW 1.2 trillion, KRW 2.4 trillion, and KRW 3.0 trillion for 2025, 2026, and 2027, respectively.
- Expected to convert to net cash after 2026 (Net cash of KRW 5.2 trillion in 2027).


We're not just heading for a memory supercycle—an ultra cycle is coming. For AI, HBM is now absolutely essential.

Summary: Bullish HBM into an 'ultra cycle': LLM demand exponentially outpaces supply; HBM4+ stacks/SoW are inevitable; China structurally behind, boosting Western HBM dominance.

Global HBM memory basket: SK Hynix, Samsung, Micron

DeepSeek paper review: 1. Why is HBM demand skyrocketing?

(1) Exponential growth in model size and context length

The paper states that the number of LLM parameters, context window length, and batch size are increasing by an order of magnitude nearly every year. As a result, memory requirements are growing dramatically.
In other words, the pace of LLM development far outstrips the advancement of HBM technology.

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(2) The pace and physical limits of HBM supply

In contrast, the increase in HBM capacity is less than 50% per year.

This means that HBM manufacturers can never keep up with the speed demanded by models and data. Naturally, the bottleneck in large-scale AI systems is shifting to memory.

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(3) Fundamental limitations of compression techniques like MLA/FP8

The paper introduces various methods such as MLA (Multi-head Latent Attention) for KV cache, low-precision (FP8) quantization, and activation quantization to significantly reduce the HBM usage per token.

MLA: Reduces memory usage by 4–5 times compared to previous methods

FP8: Cuts memory usage in half compared to BF16

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However, because the model size, number of tokens, and context length are all growing exponentially, the overall demand for HBM inevitably continues to increase.

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(4) HBM bottlenecks in real data center environments

The paper emphasizes that, in real data centers, HBM needs to be much faster than it is today in order to fully saturate 160 lanes of PCIe 6.0 (over 1 TB/s).

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In other words, meeting these requirements in the future will only be possible by increasing the number of HBM stacks, expanding the size of interposers, or adopting system-on-wafer (SoW) architectures.

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(5) The need for next-generation HBM (3D DRAM stacking, SoW, etc.)

The paper clearly states that "DRAM-stacked accelerators" and "system-on-wafer (SoW)" represent the future of LLM infrastructure.

DRAM-stacked: HBM4/4E and beyond, with 3D stacked DRAM delivering over 1.2 TB/s per stack

SoW: Integration of DRAM and logic at the wafer level, enabling memory bandwidth of several TB/s

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My thoughts after reading this paper:

We’re not just heading for a memory supercycle—an ultra cycle is coming.

For AI, HBM is now absolutely essential.

With NVIDIA Blackwell, the most expensive component is no longer the GPU chip, but the HBM chips.

In the short term, software techniques like MLA and FP8 can temporarily reduce HBM demand, but in the medium to long term, LLM trends (such as ever-larger models and longer context windows) will drive an “ultra cycle” after HBM4 (>1.2 TB/s), with more stacks, larger interposers, and highly integrated packaging like SoW.

In this respect, China is still behind. HBM packaging capabilities within China are only at about the HBM3E level.

In my view, the U.S. has also realized that memory is the alpha and omega of the AI era. The U.S. is sanctioning CXMT to prevent China from independently sourcing HBM.

Even if DeepSeek successfully develops its own ASIC, there’s a real possibility that HBM will remain the biggest bottleneck for China’s AI development.


Summary: Meritz bullish on SK Hynix and Samsung long-term: AI DC era drives stair-step DRAM demand surges, conservative supply = severe shortage, commodity memory upgrading to HBM.

Two Implications of the 'SK Ulsan AI DC Project' - Meritz Securities, Kim Sun-woo

SK Group to Build Ulsan AI Data Center (DC) Project. Collaboration with Amazon is a Surprise.

According to several media reports released today, SK Group is entering the AI data center (DC) business in Ulsan and plans to hold a groundbreaking ceremony in August. The project will be built in the Mipo National Industrial Complex in Ulsan Nam-gu, with a 40MW capacity expected to be operational by November 2027, expanding to a total of 103MW by February 2029. If this plan proceeds, the project will be the largest DC project in Korea (Figure 1).

The most surprising aspects of this project are its partners and scale. 1) It will involve collaboration with Amazon (AWS), marking the first time a major US tech company's investment and participation are planned in Korea. 2) It plans to start with a 103MW capacity and has future expansion plans to an astounding 1GW level. Considering that the Stargate Project in Texas, USA, is starting with 200MW and has long-term expansion plans to 1.2GW, this project is comparable in its gigantic scale.

Implication 1. The Front-End Industry for the Next 20 Years (PC → Smartphone → AI DC)

Our recent report, 'Computex 2025 Meritz Field Visit: AI NEXT,' emphasized the importance of AI DC in the mid-to-long-term direction of change in the semiconductor industry. Currently, society is entering a new era of IT (Intelligence Technology) from traditional IT (Information Technology) amidst an exponential increase in information, and the integration and scaling up of semiconductors are enabling this. In the past, during the first and second industrial revolutions, improvements in thermal efficiency led to productivity enhancements, which in turn led to the creation of large-scale factories and industrial complexes. Similarly, in the upcoming 'Intelligence Technology' era, large-scale factories and industrial complexes, namely AI DCs, will emerge as key and most promising industries, serving as 'intelligence' production complexes for the future.

This aligns with the concept of 'AI Factory' emphasized by NVIDIA's Jensen Huang. As the era where 'information' creates added value wanes, and 'information' transforms into 'intelligence,' large-scale intelligence industrial complexes are expected to become the front-end industry responsible for the next 15-20 years. After 20 years of the PC era from 1980, followed by 20 years of the smartphone era in the 2000s, AI DC is now expected to emerge as the most crucial front-end industry for at least the next 20 years.

Success in a front-end industry determines the success or failure of not only that company but also the entire group. In Korea, for example, the success of 'smartphones' in the 2000s led to revenue growth for over a decade for not only related affiliates like camera modules, substrates, displays, batteries, and semiconductor components, but also construction affiliates involved in component production.

Similarly, if the business viability of SK AI DC is proven, it will lead to performance improvements for all related member companies. Overall benefits are expected through vertical integration, including not only DC operation businesses but also energy suppliers, semiconductor/material/component suppliers, construction companies, and security firms. Notably, it should be emphasized that SK Group can now move beyond being merely a memory semiconductor supplier to becoming a key semiconductor procurement entity, including GPUs, thereby potentially building stronger negotiation power.

Already, AI DC investments are accelerating worldwide, not only from companies but also from sovereign funds (Figure 2). Companies newly entering the AI DC business often lack accumulated operational know-how, making various partnerships essential. According to our channel checks, SK Group has been preparing to establish a technological alliance for several years for this Ulsan AI DC investment. Technical collaborations with European power and cooling technology companies and US DC operating companies are expected to be announced.

Implication 2. Shift in DRAM Supply-Demand Cycle: Demand Curve Transforms into Stair-Step Surges

In the past, the DRAM industry, due to its commodity characteristics, primarily competed on cost rather than price. As homogeneous goods were produced, prices for all products were similar, and ultimately, large-scale production capacity (operating leverage) and technological leadership (cost reduction) were key competitive advantages.

Furthermore, discrepancies in supply and demand timing were the main drivers of DRAM cycles. In contrast to 'demand,' which fluctuated sharply in a curved manner depending on the economy, 'supply,' which had a production lag, needed to be predicted and executed 1-2 years in advance as a result of prior investment to ensure timely delivery.

After the 20-year PC era and the subsequent 20-year smartphone era, the new 20-year AI DC era is expected to form a new semiconductor demand curve. In the past, PCs and smartphones generated demand with annual sales of 300 million and 1.5 billion units, respectively. Accordingly, suppliers projected product sales for the next 12 months, considering economic and technological conditions, and executed production investments based on estimated supply availability given competitive situations.

Now, the front-end market, composed of billions of devices, is transforming into tens to hundreds of large-scale DC investments. Ultimately, the demand generated by the investment in a single AI DC site will transform from a marginal demand increase in the past to a massive, step-function surge. Simply put, the decision to invest in one AI DC facility can be described as a situation where demand for millions of smartphones suddenly emerges. The problem is that in this process, the accuracy of memory suppliers' demand forecasts will significantly decrease compared to the past, and the risk of a misforecast will become immense. Consequently, suppliers are likely to lean more towards a collective risk-aversion approach rather than the past volume-competition structure. Ultimately, the large-scale demand increase centered around AI DCs will transform the demand curve into stair-step surges, and a slow, minimum-scale preparation of supply will highly likely lead to a severe supply shortage.

We have been expecting an improvement in the DRAM cycle not only this year but also through 2026. This is because, in the new era of intelligent technology, commodity memory is transforming into specialty memory (such as HBM), leading to an increase in chip size, while suppliers continue to engage in conservative investment and face capacity constraints. With China's pursuit continuing as a supply constraint factor for some time, there is also a strong possibility that stair-step demand surges from AI DCs next year could create an unprecedented supply shortage. We anticipate mid-to-long-term benefits for domestic memory companies such as SK Hynix and Samsung Electronics.


We anticipate mid-to-long-term benefits for domestic memory companies such as SK Hynix and Samsung Electronics


stair-step demand surges from AI DCs next year could create an unprecedented supply shortage

Korea DRAM sector: Samsung Electronics + SK Hynix


Maintaining Optimism, 2H Outlook Is Key Catalyst… management confidence and improved rack shipments in the second half are expected to be stock catalysts

Summary: Shares Morgan Stanley Buy-equivalent note on NVDA: GB200 rack shipments recovering, full-year Blackwell growth on track, 2H catalysts intact.

NVIDIA: Improvement in Rack Shipment Data Eases Market Concerns; Full-Year Blackwell Growth Likely to Materialize

Morgan Stanley (J. Moore, May 12, 2025)

(1) GB200 Rack Deployment Initially Complex, Market Expectations Were Overly Optimistic

Morgan Stanley notes that NVIDIA’s GB200-based full rack solution faced a slow initial rollout due to new technologies such as ARM architecture and liquid cooling. Previously, the Taiwan market had projected 70,000–90,000 rack shipments in 2025, far exceeding Morgan Stanley’s channel-based estimate of 25,000–30,000 units. Some in the market were concerned that NVIDIA’s shipments could far outpace actual assembly capacity, leading to inventory buildup.

(2) Taiwan Shipment Data Significantly Improves, Annual Target Achievable

Latest data shows that Foxconn, Wistron, and Quanta shipped 1,500 GB200 racks in April, bringing the total for Q1 and April to 2,500 units. Even if the monthly pace remains at 1,500 units, the full-year target of 15,000 units is attainable, easing concerns about a potential “shipment cliff.” Morgan Stanley expects monthly shipments to rise further, laying a solid foundation for Blackwell GPU shipments for the full year.

(3) HGX Platform Supplements Supply, Providing a Complete Shipment Path
In addition to the NVL72 architecture, the HGX B200 platform is being deployed through customers like Coreweave in 48–64 GPU configurations. Wistron alone could drive shipments of 500,000 Blackwell GPUs in Q2 through motherboard deliveries. Combined with the estimated 320,000 units from NVL72, the total surpasses 820,000 units, approaching the quarterly 1 million-unit CoWoS capacity, dispelling concerns over a “capacity mismatch.”

(4) Maintaining Optimism, 2H Outlook Is Key Catalyst
Morgan Stanley emphasizes that its current view is more reliant on industry channel feedback rather than Taiwan shipment numbers. Channels broadly remain optimistic about demand over the next few quarters. Despite lowered guidance due to the H20 export ban and slightly below-consensus revenue, management confidence and improved rack shipments in the second half are expected to be stock catalysts. Ongoing recovery in Taiwan shipment data is a positive signal for market sentiment.

$NVDA


Summary: Shares JPMorgan's bullish AVGO/MRVL thesis on ASIC TAM raised to $30B; SK Hynix also benefits as ASIC HBM mix rises to 20-30% with improving ASP and margins.

250616_AVGO and MRVL Continue to Dominate ASICs - JPMorgan

[Key Takeaways]

(1) JPMorgan has raised its ASIC TAM (Total Addressable Market) forecast from the previous $25bn to $30bn.

(2) This upward revision is due to the expectation that the ASIC market growth rate will accelerate as hyperscalers advance their next-generation ASIC development roadmaps.

(3) Both Broadcom and Marvell Technology are presumed to have secured next-generation 2nm ASIC projects, and they are expected to maintain their overwhelming dominance in the market.

[Implication]

(1) Earlier this year, SK Hynix's ASIC revenue mix was communicated as 'double-digit, early 10%' levels.

(2) According to JPMorgan, the shipment mix accounted for by ASICs on a unit basis has already reached 40%.

(3) Currently, 1) the perception that ASICs primarily sell middle-to-low end products with lower ASPs compared to HBM integrated into AI GPUs is receding, and 2) expectations for Q (Quantity) growth are increasingly rising.

(4) In fact, the memory integrated into ASICs is also being upgraded to high-end products [HBM3E~4] comparable to the level integrated into GPU systems, and in some cases, the simple profit margin is estimated to be equivalent to or even higher than that of NVIDIA-bound products.

(5) Recently, SK Hynix has also started communicating its ASIC mix within HBM revenue at 20-30% from the previous double-digit level.

(6) As CSPs (Cloud Service Providers) increase their adoption rate of ASICs, the growth in SK Hynix's ASIC-related revenue mix is expected to 1) alleviate concerns about P (Price) [higher-than-expected ASP and excellent margins], 2) drive company-wide top-line growth due to strong Q growth, and 3) be used as a card to sustain a tight HBM supply-demand balance [used as a negotiation tool with NVIDIA].

(7) In conclusion, it is judged that the time when the performance contribution of ASICs can be visibly observed is being brought forward.

(8) An external environment that can stimulate multiples due to strong annual EPS growth or expectations of additional EPS upside seems to be unfolding.

[Contents]

(1) Broadcom's Google TPU v6 [3nm] ramp-up has begun, and the TPU v7 tape-out was recently completed.

(2) Furthermore, revenue recognition from various clients, including Meta's MTIA [3nm], OpenAI, and Softbank/ARM, is expected to fully materialize from 2026.

(3) The cycle for new ASIC design and release is becoming shorter, and the complexity of projects is increasing, with the number of transistors per chip/chiplet rising to over 150-200 billion.

(4) We estimate that Broadcom will record over $15bn in revenue this year from 3nm projects alone, and its total AI revenue [ASIC+Networking] is projected to reach $19-20bn.

(5) In addition, recent channel checks indicate that Broadcom has already successfully secured 2nm projects, including TPU v8 and the next-generation MTIA.

(6) Accordingly, full-scale mass production of new projects such as OpenAI's and Softbank/ARM's 2-3nm ASICs and Meta's next-generation MTIA is expected to begin contributing to revenue from 2H26.

(7) In Marvell Technology's case, it has also reportedly succeeded in securing 2nm projects targeting ramp-up from 2027, including Amazon Trainium 4 and Microsoft Maia Gen 3.

(8) The mass production of its first two ASIC projects, Trainium 2 [5nm] and Axion ARM CPU, is imminent, and Trainium/Maia [3nm] mass production is scheduled for 2026.

(9) Meanwhile, investors often tend to roughly consider high-speed I/O SERDES circuit technology as the sole differentiator between Broadcom/Marvell Technology and other ASIC/COT competitors.

(10) We agree that SERDES technology is a core competency, but we believe it is not the only one.

(11) This is because, in addition to SERDES IP, factors such as 1) high-performance memory architecture interfaces, 2) advanced cell libraries, 3) advanced packaging integration [2.5D, 3D SOIC, organic interposer], and 4) HBM4 logic die design capabilities broadly contribute.

(12) In fact, the only large CSPs or OEMs capable of fully undertaking cutting-edge chip design [including manufacturing → testing → packaging] internally are essentially Apple, Amazon [limited to CPU, DPU], and Cisco [switch/routing chips].

(13) Although hyperscalers are continuously increasing ASIC adoption in pursuit of 1) performance differentiation and 2) lower power consumption, the current bottleneck is their lack of capability to 100% internalize large-scale and complex SoC designs and immediately reflect them in current AI server operations.

(14) In this environment, all hyperscalers [Google/Microsoft/Meta/Amazon] and large OEMs [Nokia/Ericsson/Cisco/Samsung Electronics/Juniper/ZTE] developing ASICs have significant and minor relationships with Broadcom/Marvell Technology in the areas of 1) design IP, 2) full-chip design expertise [integrating over 100 billion transistors on a single silicon], and 3) advanced packaging integration.

(15) These factors are the driving force behind Broadcom/Marvell Technology's dominance of the entire market, with their ASIC market share nearing 70-75% [Broadcom 55-60%, Marvell Technology 15%].

(16) As of this year, ASIC XPUs are estimated to already account for a '40% mix' on a unit basis in the XPU/GPU market.

(17) The ASIC market growth has accelerated over the past few years due to the slowdown of Moore's Law and the increasing complexity of AI computing workloads, and the adoption rate of ASICs by hyperscalers is also structurally expected to rise in the future.

(18) Accordingly, we are raising our ASIC TAM from the previous $25bn to $30bn [CAGR +30%] and maintaining a positive opinion on the related value chain.

$AVGO $MRVL


the time when the performance contribution of ASICs can be visibly observed is being brought forward... An external environment that can stimulate multiples due to strong annual EPS growth or expectations of additional EPS upside seems to be unfolding


Brokers Reiterate Positive Outlook... Most brokers see upside potential for 2Q earnings revisions... AI server profitability has already surpassed that of its NB (notebook) business

Summary: Shares broker reiteration of positive outlook on Wistron: May revenue beat, 2Q upside revisions expected, AI server margins ahead of notebook business.

🔹 Wistron (3231): Brokers Reiterate Positive Outlook
• May revenue exceeded NT$200 billion, bringing QTD performance to 85% of U.S. broker estimates, significantly ahead of expectations.
• Most brokers see upside potential for 2Q earnings revisions.
• However, some U.S. investment banks noted that peer companies may not replicate the same month-on-month growth because:
• Wistron is shipping L10-level compute trays,
• while peers are delivering L11-level racks,
• which require longer assembly and testing time.
• Regarding Wistron itself, brokers observe that U.S. customer pull-ins remain smooth,
with an original forecast of 500–1,000 GB200 units to be shipped during May–June, which is tracking in line with expectations.
• New projects with Oracle and Cisco are also in the pipeline.
• Brokers emphasize that AI infrastructure build-out is not a linear process,
but for Wistron, AI server profitability has already surpassed that of its NB (notebook) business.

早安！6/5 外電綜合整理

-3231緯創: 券商重申正向
5月營收一舉超過2000億，QTD達成美系券商預估數85%，表現大幅優於預期，券商們都認為2Q還有上修空間。不過美系大行認為同業未必有如此月增幅，原因在於緯創出是L10的compute tray，而同業出L11的 rack，後者的組裝跟測試需要花更多時間。