J.P. Morgan forecasts 2026/27 revenue growth of +31%/+25%, which exceeds consensus (~+27%/+22%), and JPM's 2026/27 gross margin estimates of ~62% are also above consensus (~60%)

Summary: JPM upgrades TSMC estimates above consensus on 2026/27 revenue growth, gross margins, and data-center AI CAGR; expects positive 1Q26 GM guidance surprise.

J.P. Morgan: Upward Revision of TSMC Estimates

Our analyst Gokul has recently upgraded his TSMC estimates. The key differences versus consensus are that J.P. Morgan (JPM) forecasts 2026/27 revenue growth of +31%/+25%, which exceeds consensus (~+27%/+22%), and JPM’s 2026/27 gross margin (GM) estimates of ~62% are also above consensus (~60%).

JPM’s 2026/27 Capex estimates are US$48bn/US$55bn, which are higher than consensus (~US$46bn/US$52bn) despite no upward revision to the prior 2026 figure in light of cleanroom space constraints.

We estimate 4Q25 gross margin at 62%, above consensus (~61%). We also expect TSMC to guide 1Q26 gross margin to 61-63%, which is below our estimate of 63.1% but above consensus (~60%) and could be a positive surprise for the market. The backdrop is that ~10% of total orders are being processed on an expedited basis with substantial pricing premiums (half at ~50% premium “hot runs” and the other half at ~100% premium “super hot runs”).

While the market is modeling gross margins below 60% for 2Q-4Q, Gokul forecasts 62.7%/61.2%/60.8% respectively, reflecting the high base from 1Q, which may lead market participants to question the sustainability of this pricing model.

We expect TSMC to guide 1Q26 revenue roughly flat QoQ (our forecast +3% / consensus +1%), full-year 2026 USD revenue growth in the mid-20% range, and Capex of US$46-50bn. We also expect no specific guidance on CoWoS capacity growth (our estimate +69%), and for the 2024-29 data center AI revenue CAGR to be guided to mid-50% (versus our model of 57%).

$TSM


Summary: Long NVDA: Rubin+LPX closes the startup speed gap, system-level integration gives durable structural advantage; AI accelerator startups' addressable space is shrinking.

structural marker; pre-classified as canonical yfinance ticker

GTC 2026 Recap: Nvidia Closes Its Biggest Gap — Dramatically Narrowing the Key Advantage of AI Accelerator Startups

Looking back at my pre-GTC preview, the directional calls and technical roadmap were largely correct. What Nvidia ultimately delivered was more elegant than I anticipated: not only is prefill handled on the GPU, but the Attention phase of decode is also kept on the GPU (that part I didn't see coming) — only the MLP phase of decode is offloaded to the LPU.

This bears a striking resemblance to MatX's approach: Weights sit in SRAM, KV cache sits in HBM.

The logic is sound. The Attention phase demands enormous KV cache — easily tens to hundreds of GB — which is simply beyond what LPU SRAM can hold. Keeping that portion on HBM is the more rational choice.

This maps neatly onto the emerging trend in agentic workflows: longer multi-turn conversations, exploding long-context KV cache. Even when high batch concurrency generates massive KV cache, HBM can absorb it. As context length grows, all incremental cost falls on GPU HBM — LPX is entirely static, unaffected, scaling only with model weight size.

This lets the LPU's precious 128 GB of SRAM focus exclusively on the fixed weights of the FFN/MLP phase. FFN accounts for over 50% of the entire decode stage on a GPU — in short-context scenarios, it can exceed 60%. Accelerating FFN on the LPU by several multiples is a highly favorable trade.

The potential drawback of this design is that a typical transformer decode pass spans many layers — say 80. That means Attention and FFN alternate 80 times, i.e., tensors must shuttle between GPU and LPU 80 times per token. Even over low-latency Nvidia Spectrum-X Ethernet, generating a single token accumulates 80 round-trip GPU–LPU latencies. That is non-trivial overhead.

Under this new architecture, assuming a 40/60 Attention–FFN split, with FFN accelerated by several multiples on the LPU, peak theoretical throughput improvement reaches well over 2× compared to Rubin NVL72.

Breaching 1,000 tokens/sec at peak speed, while still maintaining commercially meaningful throughput, is significant. On Blackwell, hitting 400–500 tokens/sec forced you down to handling very few concurrent requests — a massive waste of GPU resources. Now, even at 1,000 tokens/sec, a meaningful batch size can be sustained, finally unlocking real commercial value.

The slide shows that at 400 tokens/sec, Rubin + LPX delivers a 35× throughput improvement — classic token economics. At that token velocity, it represents 35× more commercial value versus Blackwell.

So what does this mean for startups — Cerebras, d-Matrix, MatX, SambaNova — now that Nvidia has closed this gap?

The core value proposition of these startups has always been a speed advantage or cost advantage in specific scenarios.

In large-batch (high-concurrency) settings, GPUs exceed the ridge point and approach high arithmetic intensity utilization — both cost and speed metrics become unfavorable for startups. Their survival niche has always been: workloads concentrated in small-batch, ultra-low latency, extreme token speed, where cost is no object. GPUs are deeply inefficient here and simply cannot match the required token rates.

Cerebras: Pure speed. Wafer-scale die with massive on-chip SRAM (40 GB) eliminates inter-chip communication — the dominant bottleneck — delivering extremely high token rates at small batch sizes. Cost competitiveness is nonexistent: a single CS-3 system runs $2.3M, far exceeding an equivalent GPU cluster — roughly 10× the cost for ~10× the speed versus H100.

d-Matrix: High speed + small-batch scenarios. In-memory compute reduces data movement; utilization exceeds GPU efficiency in small-batch decode, giving it competitive perf/watt in that regime. The recently introduced 3D stacked DRAM addresses capacity and bandwidth scaling as larger reasoning models and higher token consumption put pressure on the stack.

SambaNova: Targets enterprise on-premises deployments running multiple mid-size models concurrently. GPUs suffer significant context-switching overhead in this scenario, while SambaNova's RDU delivers better perf/dollar. The advantage is fundamentally a cost advantage in a specific use case — the raw speed lead is not as pronounced.

MatX: Partitionable systolic array + hybrid SRAM/HBM — conceptually similar to Nvidia's new heterogeneous architecture. The standout feature is achieving Weights-in-SRAM and KV-cache-in-HBM within a single chip. Critically, by keeping everything on one chip, it eliminates the 80-layer GPU–LPU inter-chip round trips described above — so it retains a speed edge, but scalability may be more constrained than a GPU + LPU array.

Bottom line: With Rubin + LPX, the small-batch, low-latency, extreme-speed gap that previously favored startups has been largely closed. The viable addressable space for these companies is shrinking.

One more observation from my preview: speculative decoding using the LPU as the draft model and the GPU for verification — I predicted this would yield dramatic acceleration. That call landed squarely. Nvidia's official blog devoted an entire dedicated section to it: "LPX generates draft tokens rapidly using its low-latency architecture. Rubin GPUs verify and finalize tokens efficiently."

The other item from my preview — CPX (Content Phase aXcelerator, a compute module purpose-built for the compute-bound characteristics of prefill) — appears to have vanished entirely from GTC. Not a single word. Does that mean CPX is cancelled?

I don't think so, necessarily.

Current prefill and decode already run in a disaggregated structure — a subset of GPUs handles prefill, another handles decode. Replacing the prefill-side GPUs with CPX is architecturally cleaner and would accelerate the prefill phase, though it adds cost as an additional chip. CPX has no fundamental conflict with the current Rubin + LPX architecture — it is simply a drop-in replacement for the prefill GPU partition. When speed optimization demand resurfaces, CPX may well return.

Same reflection as last time: every shift in computing paradigm triggers a wave of semiconductor startups. But as the software and application landscape gradually converges, the outcome is invariably that the platform incumbents absorb the innovations through acquisition — scaling parameters, deepening system integration, lowering cost, improving power efficiency and benchmarks — until startups lose their independent reason to exist.

In the early days of mobile internet, it was the same: a fragmented ecosystem of independent AP, baseband, ISP, and GPU vendors. But the ultimate winners were the companies that integrated GPU, ISP, modem — everything — into a single SoC, completing the heterogeneous compute platform at the system level.

Apple acquired PA Semi's CPU team, Infineon's modem, and hollowed out Imagination's GPU. Qualcomm acquired ATI's mobile GPU, Atheros's Wi-Fi, Nuvia's CPU, CSR's Bluetooth/DSP. These are all canonical examples.

The complexity of heterogeneous inference is only increasing. Companies capable of system-level integration will have a durable structural advantage — the same logic that governed the mobile SoC era. Nvidia's attempted ARM acquisition (blocked), Mellanox acquisition, and Groq acquisition are just the opening moves of this new historical cycle.

$NVDA

复盘GTC 2026：Nvidia补上了短板，大幅削弱了各个AI 加速器 startup最大的优势--token速度

回顾这篇GTC前瞻，方向预测和技术路线写的没啥大问题，最后Nvidia给出的解法比我想象的更为精巧：不仅是prefill放在GPU上，decode阶段Attention阶段也放在GPU上(这点没想到)，只把decode的MLP阶段放在LPU上做 https://t.co/0nX5A10pXS


Summary: Bullish Nikkei on Takaichi landslide win securing mandate for fiscal and monetary stimulus.

@SteveMatthews12 @takaichi_sanae Since Takaichi won a landslide victory in the general election and has secured the political mandate and governing capacity to push through more fiscal and monetary stimulus, the Nikkei has no choice but to go up.

@jukan05 @takaichi_sanae For someone not following Japan, why is this bullish?


I'd pick TSMC and the memory names. But the foundries that can actually manufacture those 'brain' logic chips, and the memory suppliers, feel much harder for China to truly penetrate in the long run.

Summary: Long TSMC and memory names as robotic supply chain plays — logic foundry and memory are hardest for China to penetrate long-term.

Someone asked me, “Jukan, what is the best robotic supply chain play, in your opinion?” So I tried to think it through and come to my own conclusion.

I’d pick TSMC and the memory names.

I think most robot components can ultimately be produced in-house. People love to talk about reducers having a huge technological moat, but when I ask people in the industry, the answer to “is it really that hard to make?” is usually no — it’s not that impossible.

In the end, components other than the robot’s “brain” — meaning the logic chip and the memory chip paired with it — will have to compete head-on with Chinese companies. And honestly, I’m not sure you can maintain attractive profitability while competing with China in those areas.

Even for the logic chip itself, China might eventually be able to design it domestically.

But the foundries that can actually manufacture those “brain” logic chips, and the memory suppliers, feel much harder for China to truly penetrate in the long run. That’s my conclusion.

And yeah — it seems I ended up recommending the “safer” plays rather than the best return opportunities. But I genuinely worry about China dominating humanoids the way it did EVs. The components that go into EVs and humanoid robots are highly similar.

China has already taken over large parts of the supply chain — starting with smartphones and extending into EVs — and I can’t shake the feeling that it won’t take long before it also takes over the humanoid supply chain.


I'd pick TSMC and the memory names. the memory suppliers, feel much harder for China to truly penetrate in the long run

Memory sector basket: Micron, SK Hynix, Samsung


Summary: Bullish ASML and Intel: Broadcom-designed AWS chips use Intel EMIB packaging; Nvidia's next chip reportedly headed to Intel 14A.

industry='Semiconductor Equipment & Materials'

This is a very bullish piece on ASML, but if you look closely, it’s also bullish on Intel.

According to the article, Broadcom-designed AWS chips will be packaged using Intel’s EMIB. It even claims that Nvidia’s “Nintendo Switch 3” chip will go into mass production on Intel 14A.

You should definitely read it.

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https://t.co/guzhfDNyK6


Summary: Author highlights Samsung's hybrid bonding commercialization plan for HBM4E, tagging BESI as the implied beneficiary.

* Samsung: Planning to commercialize hybrid bonding in part for HBM4E.

$BESI


we remain skeptical... Intel has a severe shortage with no revenue growth... foundry losses remain at a $10 bn run rate... AMD captured all of the unit growth... AMD's server products on 5 nm and even 7 nm likely outperform Granite Rapids, and even potentially Diamond Rapids

Summary: Shares Morgan Stanley's skeptical INTC review: AMD winning server share on superior products while Intel faces foundry losses, no revenue growth, and structural challenges.

Morgan Stanley’s comment from its Intel earnings review:

As the microprocessor market has been in shortage since September, some of the focus has shifted to supply, which could favor Intel - but we remain skeptical. The server market is strong, but at least some of this is a function of Intel's lack of supply growth. With no revenue growth in 2H25, AMD captured all of the unit growth, which surprised them - our sense has been that AMD's tight supply demand situation on server CPU has more to do with demand upside than any real constraints.

Looking forward, Intel should have better than seasonal supply growth beyond 1q, but AMD should as well. While 3 nm wafers remain tight, we believe that AMD's server products on 5 nm and even 7 nm likely outperform Granite Rapids, and even potentially Diamond Rapids given Intel's concerns around the lack of symmetric multithreading - the best processors will still likely win.

Full year commentary on the call and the callback points to double digit growth for data center in CY26 (we were at +12% coming into the year), with declines in PCs given the memory shortage impact (we were up slightly). There will be price increases given ongoing shortages but we would think of them as mild, given competitive issues and supply shortfalls, and if the shortage eases in PCs prices will be weaker there as well.

This backdrop does create incremental challenges for the foundry prospects. In addition to just proving their basic foundry capabilities, customer objections to using Intel have been an on-again, off-again commitment to the foundry business, and the need to compete with the Intel product group for wafers. The fact that Intel has a severe shortage with no revenue growth will not ease those concerns - and while the current management team isn't to blame, we have had several shortages over the last 8 years as revenues have declined from $70 bn to $52 bn. Samsung remains the most viable alternative to TSMC, and TSMC's buildout in Arizona reduces the geopolitical imperative. The foundry losses remain at a $10 bn run rate, and plans to improve that are going to come mostly from charging more for the wafers, which puts the burden on the product group to build higher value products - which remains the key driver here.

$INTC


AMD's tight supply demand situation on server CPU has more to do with demand upside than any real constraints... AMD should as well [have better than seasonal supply growth]... the best processors will still likely win


Fundamentals clearly remain unattractive here... We don't find ourselves tempted by the President's whims. We rate Intel Market-Perform with a $35 TP.

Summary: Shares Bernstein Market-Perform on INTC with $35 TP; fundamentals unattractive on PC demand, share losses to AMD/Arm, margin headwinds, and weak foundry progress.

Bernstein: Intel (INTC, Market-Perform, $35 TP) - Trump wants the stock to go up...

Fundamentals clearly remain unattractive here. Sustainability of core markets, especially PC, remains questionable as the current windows EOL won't last forever and the trajectory of the AI PC refresh cycle remains up for debate, plus rapidly increasing memory prices seem likely to put a further damper on demand into 2026. Furthermore, the company has continued to lose substantial amounts of share both to traditional competitors like AMD as well as ARM (with AAPL taking 10%+ away over the last several years, and now with new entrants like QCOM, and potentially NVDA and Mediatek, entering the mix). And in datacenter while we do believe server CPU growth can be strong as upgrades finally come through the company continues to bleed share in traditional x86 CPUs to AMD, while x86 in general loses share to Arm as hyperscalers increasingly deploy their own internal solutions, and CPUs continue to lose share to GPUs and accelerators with the company having effectively zero response to the advent of AI (Exhibit 9).

While the company is ramping 18A and suggesting they feel more positive around 14A, supply constraints appear primarily on 10/7nm (where demand is higher because customers are less enthused by Intel's newer products) and seems likely to cost further share. It also sounds like 18A yields, while “adequate” to ship, still leave something to be desired (they suggested they wouldn't really be at mature levels until the end of next year), and 14A remains a long way off. And it increasingly sounds like 2026 margins could be faced with a variety of headwinds.

That being said, the stock is clearly NOT trading on fundamentals. And we admit that “Donald Trump wants the stock to go up” may indeed be viable bull case (for now it clearly has been). (Exhibit 10). But while we wouldn't necessarily talk you out of it if you are feeling a bit degenerate it's not our kind of bull case, and fundamentals there appear unattractive to us regardless of what the President might desire.

We don't find ourselves tempted by the President's whims. We rate Intel Market-Perform with a $35 TP.

$INTC


Summary: Samsung strike risk could halt memory lines for months; SK Hynix and Micron are near-term beneficiaries.

I feel like I’ve been talking too much about myself, so to shift the conversation back to memory…

It looks like Samsung’s semiconductor division may genuinely be heading toward a strike. The union is demanding a performance bonus equal to 20% of operating profit, while management is holding firm, saying it cannot remove the cap on bonuses.

The union seems willing to do whatever it takes to eliminate the bonus gap with SK Hynix. There is a real possibility that Samsung’s memory production lines could actually be halted.

Once a line is shut down, it can take as long as two months to restart. The losses could amount to tens of billions of dollars.

That would be good news for Samsung’s competitors. In the near term, SK Hynix and Micron look like the obvious beneficiaries.


Samsung Electronics started preparations to expand its TC bonder suppliers… The leading candidate within Samsung is Singapore-based ASMPT

Summary: Samsung diversifying TC/hybrid bonder supply chain to ASMPT and Besi beyond existing suppliers, implying new revenue opportunities for both.

ASMPT listed on Hong Kong Exchange 0522.HK

Samsung Eyes Diversification of TC Bonder Supply Chain… In Discussions with ASMPT

Samsung Electronics has begun efforts to diversify its supply chain for thermo-compression (TC) bonders used in high-bandwidth memory (HBM) and 3D packaging. Attention is now focused on whether this will lead to changes in the supply of next-generation hybrid bonders.

According to industry sources on the 21st, Samsung Electronics started preparations to expand its TC bonder suppliers as early as the fourth quarter of last year.

The leading candidate within Samsung is Singapore-based ASMPT. The company is better known for supplying equipment to SK Hynix’s HBM lines than to Samsung Electronics.

An industry source familiar with the matter said, “I understand that Samsung Electronics is actively negotiating with ASMPT to supply a range of bonding equipment it needs, including TC-NCF (non-conductive film) bonders.” The industry sees a strong possibility that cooperation could extend to “fluxless bonders,” which improve process efficiency in TC bonding.

Samsung Electronics is also reportedly showing strong interest in diversifying the supply chain for next-generation hybrid bonders. In addition to exploring cooperation with Netherlands-based BE Semiconductor Industries (Besi)—which has already supplied hybrid bonder prototypes—centered around its subsidiary Semes, Samsung is understood to be holding related discussions with ASMPT as well.

Bonders are taking on growing importance in Samsung Electronics’ semiconductor production lines. The currently commercialized TC-NCF bonder bonds multiple semiconductor chips together using heat and pressure, much like an iron.

Samsung used the TC-NCF process to stack and bond multiple DRAM chips in its latest HBM4, which is built with 10nm-class 6th-generation (1c) DRAM. As future HBM stacks grow taller, equipment capable of bonding chips with uniform pressure without causing warpage will become increasingly critical.

To date, Samsung Electronics has primarily relied on equipment from its subsidiary Semes and Japan’s Shinkawa in the TC-NCF bonder segment. It is known that equipment from both companies is extensively deployed at Samsung’s Cheonan campus, where advanced packaging (AVP) processes are concentrated.

Samsung’s move to broaden ties with foreign bonder makers such as ASMPT and Besi, beyond its existing two suppliers, is interpreted as an effort to secure alternatives to overcome the sharply rising bonding difficulty expected after HBM4. It also reflects a judgment that advanced heterogeneous integration packaging—connecting substrates to dies and dies to dies—is needed not only for HBM but also for system semiconductors.

One industry source explained, “For both the already-commercialized TC-NCF bonder and the early-stage hybrid bonder, the key challenge for suppliers is how much high-level synergy they can create with Samsung Electronics.”

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https://t.co/9UGzRs7AdH


exploring cooperation with Netherlands-based BE Semiconductor Industries (Besi)—which has already supplied hybrid bonder prototypes


Summary: China set to approve Nvidia H200 purchases this quarter; Alibaba and ByteDance each eyeing 200k chips — bullish demand signal for NVDA.

It was reported that Alibaba and ByteDance each said they wanted to order 200,000 H200 chips.

$NVDA https://t.co/vX3uGFq1j5

*CHINA TO APPROVE SOME NVIDIA H200 PURCHASES AS SOON AS THIS QTR


Summary: Bullish MU: DRAM prices up ~90-95% this quarter and again next, implying memory prices ~4x end-of-year levels by June.

Taiwan's Winbond: The DRAM supply shortage is expected to persist, with memory prices projected to surge 90–95% this quarter. Furthermore, the magnitude of price increases next quarter is likely to remain at a similar level.

This means prices are not just nearly doubling this quarter—memory prices could effectively double again in Q2. This implies that by the end of June, memory prices could reach nearly 4x the levels seen at the end of last year.

$MU


It doesn't make sense for Intel to be more expensive than TSMC.

Summary: INTC is overvalued relative to TSMC; implied bearish on Intel's relative valuation.

"It doesn’t make sense for Intel to be more expensive than TSMC."

$INTC https://t.co/shpRuHGHqy


Summary: Shares analyst thesis that Samsung Foundry swings to profit next year on Tesla AI chips, Qualcomm/AMD orders, and Taylor fab ramp — implied long Samsung Electronics.

Samsung Foundry Eyeing 'Turnaround to Profit' Next Year Amid Potential Orders from Qualcomm and AMD for Tesla Chips

Samsung Electronics' Foundry Division, long considered the "troubled child" of the Device Solutions (DS) department, is projected to swing back into the black next year. This optimistic outlook is fueled by the mass production of Tesla's next-generation AI chips and the increasing likelihood of securing orders from global tech giants such as Qualcomm and AMD. As yields for advanced processes below 3nm stabilize and the Taylor, Texas fab nears operation, Samsung is aggressively pursuing contracts with global big tech firms.

According to industry sources on the 26th, expectations are rising for a turnaround in the foundry business, which has recorded trillions of won in losses since 2022. Kim Dong-won, head of research at KB Securities, stated, "Samsung Foundry's performance is expected to shift from a 7 trillion won loss last year to a profit next year, driven by increased capacity utilization from Tesla AI chip supplies." A source familiar with Samsung Foundry's operations added, "We are accelerating our order-taking efforts with the goal of turning a profit next year. Beyond the stabilization of sub-3nm process yields, the utilization rates for highly profitable 4-8nm nodes are currently at maximum capacity."

The turnaround projection aligns with the operational timeline of the Taylor fab. Samsung Electronics is investing over $37 billion (approx. 54 trillion KRW) to establish a massive foundry production base in Taylor, Texas, by 2030, with mass production slated to begin in the second half of this year. Final preparations, including the deployment of engineers for pilot production, are currently underway. The primary goal is to manufacture chips for major U.S. tech companies.

Profitability is expected to improve significantly as the Taylor fab begins producing Tesla's AI chips. Samsung Foundry plans to mass-produce the AI5 and AI6 chips next year—the "brains" behind Tesla’s Optimus humanoid robots, Robotaxis, and autonomous driving systems. Notably, reports suggest that Samsung Foundry will be the exclusive producer of the AI6 chip.

The possibility of securing orders from Qualcomm and AMD is also increasing. Qualcomm, a long-standing partner and a key supplier for Samsung’s Mobile eXperience (MX) division, had previously shifted orders away due to low yields in Samsung's advanced processes. However, as Samsung's 2nm process stabilizes, the chances of a return are growing. Additionally, AMD, a long-term customer of TSMC, is reportedly seeking to diversify its foundry partners.

TSMC’s current capacity saturation, despite its 70% market share, is also working in Samsung's favor. As big tech demand concentrated on TSMC for stable sub-3nm nodes, its production lines reached their limit, causing prices to skyrocket and lead times to lengthen. This has prompted customers to seek Samsung Foundry as a viable alternative. Furthermore, utilizing the Taylor fab allows companies to align with the "Made in USA" policy of the Trump administration.

Increased utilization of 4-8nm nodes has also bolstered profitability. These mature nodes offer stable yields compared to cutting-edge processes and are considered competitive against TSMC in terms of performance and pricing. Samsung currently utilizes these nodes to manufacture High Bandwidth Memory (HBM) logic dies, as well as chips for IBM and Nintendo.

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https://t.co/215pZNhRMS


Summary: Relays Goldman Sachs Buy call on AAPL with $320 PT; recommends buying dip before Jan 29 earnings on +11% revenue growth forecast and AI super-cycle thesis.

>> Apple Stock Falls for 8 Consecutive Weeks… Goldman Sachs Recommends ‘Buy the Dip Before January 29’ Despite Memory Cost Pressures

• Apple shares have declined for 8 straight weeks, marking the longest consecutive losing streak since May 2022. Notably, among the ‘Magnificent 7’ stocks since July last year, Apple has experienced the most pronounced fund outflows.

• Market concerns are mounting over margin pressure on Apple products, including the iPhone, as memory prices have surged 40–50% due to explosive demand for AI hardware. This is creating short-term caution around earnings and profitability.

• Nevertheless, Goldman Sachs is taking a contrarian stance. The firm forecasts +11% YoY revenue growth for Apple’s fiscal Q1 results scheduled for release on January 29, and views the full rollout of AI features and replacement demand (super cycle) as key mid- to long-term growth drivers. Goldman Sachs maintains a $320 price target and continues to see the current weakness as a buying opportunity ahead of the earnings report.


Summary: Bullish Kinsus on AI-driven ABF substrate demand, double-digit 2026 revenue growth, and K6 expansion; also bullish Unimicron on accelerating price hikes from 2Q26.

Kinsus Interconnect Technology 3189.TW Taiwan

▶ AI boom lifts substrate maker Kinsus as prices and demand rise

∙ Driven by growing demand for AI GPUs, CPUs, and ASICs, Kinsus is expected to benefit from new ABF substrate capacity coming online, along with improving revenue and product mix, with double-digit revenue growth projected for 2026.

∙ The share of AI-related revenue is expected to expand from around 5–10% currently to 10–15% by the end of 2026, with price hike benefits expected to be reflected more meaningfully in the second half of 2026.

∙ Upstream, shortages of high-end fiberglass cloth continue, leading to longer CCL lead times and rising raw material costs, including precious metals and prepreg, which are increasing pricing pressure for both ABF and BT substrates.

∙ Unimicron is expected to accelerate price hikes starting in 2Q 2026, while Kinsus has indicated ABF price increases of around 5% in 1H26 and up to 7% in 2H26, with profitability likely to improve alongside a better product mix.

∙ The biggest bottleneck remains the shortage of T-glass fiberglass cloth. With supply concentrated around Ibiden and capacity expansion limited, CCL lead times have extended to as long as 24 weeks.

∙ As a result, customers are placing orders earlier and becoming more directly involved in procurement.

∙ Taiwan Glass, Nanya Technology, and Fulltech Fiber Glass are attempting to enter the high-end fiberglass cloth market, but their impact on easing supply constraints is expected to remain limited due to scale and yield challenges.

∙ There is growing discussion across the supply chain that fiberglass material shortages could persist through 2027, potentially capping upside for ABF and BT substrate market growth.

∙ Kinsus’s current revenue mix is approximately 30% ABF substrates, 40% BT substrates, and 20% contact lenses, and all business segments are expected to grow in 2026.

∙ Over the next three years, the company plans to pursue an ABF substrate-focused expansion strategy, with the K6 plant in Yangmei, Taoyuan set to serve as a key production hub.

∙ Phase 1 is already running at full utilization and is expected to drive growth in 2026.

∙ Phase 2 is scheduled to begin operations in 2027 and is expected to raise monthly capacity by around 25% to roughly 5,000 units.

∙ In addition, Kinsus has approved NT$23.5 billion in capex, with investment set to rise from NT$7–8 billion in 2026 to around NT$10 billion in 2027.

∙ The Yangmei site also has space secured for a Phase 3 expansion, leaving room for additional investment after 2028 depending on AI chip demand.

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https://t.co/W1vf9iMJHp


Unimicron is expected to accelerate price hikes starting in 2Q 2026

Unimicron Technology listed on TWSE as 3037.TW


Summary: Endorses bullish stance on ASML citing TSMC's rising EUV tool demand (25/36 tools in 2026/2027) driving capex cycle.

"Based on our estimates of TSMC’s EUV demand of 25/36 tools in 2026/2027, we expect capex to reach US$54 billion in 2026 and US$64 billion in 2027, maintaining a normal level of capital intensity."

Bullish $ASML

GF Overseas Electronics Communication

TSMC (2330TT Buy): Strong Capex in an Unprecedented AI Cycle

Raising Target Price to NT$2,325
TSMC reported strong 4Q25 results, with full-year revenue guidance in line with expectations, while the capital expenditure guidance of


Summary: EO Technics and Disco named as sole beneficiaries of Samsung's femtosecond laser dicing ramp for HBM4, with demand set to intensify.

EO Technics 039030.KQ Korea KOSDAQ laser equipment

Samsung Electronics to Enhance HBM4 Quality with Next-Generation Wafer Dicing Process

Samsung Electronics is transitioning its wafer dicing process — a critical step directly tied to semiconductor quality and yield — to a next-generation approach. The company plans to expand its application of "femtosecond (one-quadrillionth of a second) laser" technology, which was first introduced last year targeting High Bandwidth Memory (HBM4), in a move to assert dominance in the HBM4 market through dramatically improved cutting precision.

According to industry sources on the 23rd, Samsung Electronics is placing orders for wafer dicing equipment equipped with femtosecond laser technology. The equipment covers both "grooving" — which scores the wafer surface — and "full-cut" tools, with the initial procurement volume standing at a minimum of 10 units. The equipment is slated for installation at Samsung's Cheonan campus, where advanced semiconductor packaging operations are conducted, and purchase orders (POs) are currently being prepared.

Samsung is also reviewing the option of placing additional orders to secure as large a volume as possible. Given that lead times for wafer dicing equipment exceed eight months, the intent is to secure supply proactively while substantially expanding the scope of femtosecond laser-based dicing processes. A well-placed industry source noted, "Even after the initial introduction, Samsung Electronics is in ongoing discussions with partners to continue scaling up femtosecond grooving and full-cut equipment," adding that it represents "an attempt to convert the majority of wafer dicing processes to the next-generation approach."

In semiconductor manufacturing, a dicing process — which separates wafers into individual chip units (dies) — is essential following the front-end process where circuits are etched onto wafers. Conventional dicing has largely relied on mechanical cutting via diamond wheel blades. While lasers have been used in some cases, the laser pulse duration — the key measure of precision — has remained at the nanosecond (one-billionth of a second) level.

Femtosecond lasers generate pulses at the one-quadrillionth-of-a-second scale, enabling far finer cuts compared to both conventional mechanical methods and nanosecond lasers. The technology can cut without affecting the ultra-fine circuitry and interconnects of advanced semiconductors, while also minimizing particle contamination generated during the dicing process — ultimately maximizing the quality of the finished semiconductor.

Samsung Electronics first introduced femtosecond laser dicing in Q2 of last year. At the time, only a handful of units were deployed, but having demonstrated improvements in performance and productivity, the company has decided to scale up adoption. Samsung plans to apply femtosecond laser dicing to HBM4 processes as a priority, with the goal of improving HBM4 yield and productivity.

Samsung recently commenced mass production shipments of HBM4. Demand for femtosecond laser dicing is expected to grow in tandem with the ramp-up schedule for full-scale mass production. The company is also reportedly evaluating the introduction of femtosecond laser dicing for products beyond DRAM, including NAND flash and system semiconductors.

Samsung's femtosecond laser dicing supply chain is expected to be handled by domestic firm EO Technics and Japan's Disco. An industry source commented, "Competition between EO Technics and Disco for femtosecond laser dicing orders is set to intensify," adding that "demand for femtosecond laser dicing is rising not only at Samsung Electronics but also at other domestic and international semiconductor manufacturers."

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https://t.co/pZmxwDOXv0


I'm going to buy more Intel with this money

Summary: Author plans to buy more Intel with received funds; bullish on INTC.

Thank you, @elonmusk! I’m going to buy more Intel with this money. https://t.co/SV97sJc9O6


memory suppliers are leading the LTAs. NCNR contracts with no price negotiation are expanding to PCs, mobile, and commodity DRAM

Summary: Supply shortage flips LTA power to memory chipmakers; shorter contracts + NCNR expansion locks in volume and pricing power for Samsung, Hynix, and MU.

Supply Shortage Reshapes LTA Dynamics… Contract Periods Shrink and Power Balance Flips

As the supply shortage drags on, the trading order in the memory semiconductor market is rapidly being reorganized. With the semiconductor supply crunch persisting over an extended period, the initiative in long-term supply agreements (LTAs) is shifting from customers to semiconductor suppliers. In the past, smartphone and PC manufacturers seeking stable procurement were the ones to propose long-term contracts first, but recently memory chipmakers such as Samsung Electronics and SK hynix have been presenting contract terms first, signaling a shift in the market's balance of power.

According to industry sources on the 9th, the contract periods of LTAs recently signed by Korean memory chipmakers are getting shorter. LTA durations vary from six months to one year depending on the product and end customer, but industry insiders universally say these periods have been shrinking lately.

The analysis is that as the semiconductor supply shortage continues, suppliers are opting for shorter contract periods. In a rising-price environment, shorter contract durations allow suppliers to reflect updated pricing more quickly, giving them an advantage in negotiations.

The initiative in LTAs is also shifting from customers to memory suppliers. Previously, it was common for smartphone and PC makers to propose long-term supply agreements first to secure volume, but recently Samsung Electronics and SK hynix are increasingly taking the lead in setting contract terms and structures. As the semiconductor supply shortage extends, the center of gravity in business relationships is tilting toward suppliers.

Lee Jong-wook, a researcher at Samsung Securities, analyzed: "It was customers who first proposed long-term supply agreements, but now memory suppliers are leading the LTAs. NCNR (non-cancellable, non-returnable) contracts with no price negotiation are expanding to PCs, mobile, and commodity DRAM, causing the volume of available spot supply in the market to shrink dramatically."

Stockpiling behavior is also emerging. According to market research firm TrendForce, smartphone manufacturers are being extremely cautious about immediately reducing their LTA-based memory purchase volumes this year. TrendForce noted: "Concerned about further price increases and potential supply shortages, they are preemptively prioritizing resource procurement to avoid future allocation restrictions." In other words, despite slowing global smartphone demand and a negative market outlook, companies are stocking up on semiconductors out of fear of supply constraints and additional procurement cost increases ahead.

For smartphone makers, rather than cutting production or trimming contracted volumes right away, the more pressing concern has become whether they can receive memory shipments on time going forward. The worry is that tampering with contracts now could mean failing to secure adequate volume next quarter or next year, or having to re-contract at even higher prices. So even in an unfavorable market, they appear to be maintaining their long-term supply agreements as-is.

Memory chipmakers share this view. During the conference call following its Q4 earnings announcement, SK hynix explained: "In the past, LTAs had loose volume commitments and were highly flexible depending on market conditions. The LTAs being discussed recently are not mere purchase intentions but arrangements where customers and suppliers firmly commit volumes to each other." This implies that from the supplier's perspective, demand visibility has become far more important than before.

An industry insider said: "In this unprecedented memory supply shortage, the power dynamic between buyer and seller is reversing. Even as consumer electronics demand slows, semiconductor supply contracts are being maintained — and these contracts are, in turn, entrenching the supply shortage and price increases in a self-reinforcing cycle."

$MU $SNDK

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https://t.co/Yk7ywEN1Er


Summary: GF reiterates Buy on Broadcom, raises PT to $450 on higher TPU forecasts, AI revenue upgrades, and strong external/Anthropic demand.

GF Overseas Electronics & Communications
☄ Broadcom (AVGO US Buy): Raising 2027 TPU Forecast

☀ Maintain Buy; raise target price to US$450:
We forecast total TPU shipments to reach 4.5 million units in 2026 and 7.9 million units in 2027, above our previous estimates of 4.5 million and 6.0 million, respectively. This upward revision is mainly driven by strong demand from external customers, with TPU shipments designed by Broadcom expected to reach 4.1 million units in 2026 and 5.8 million units in 2027. Based on this, we raise our AI-related revenue forecasts for FY2026/FY2027/FY2028 to US$50bn/US$110bn/US$130bn, and revise FY2026/FY2027 EPS forecasts to US$10.8/US$18.0. Given lower market risk appetite and multiple compression across AI fabless names, we reduce our target P/E from 27x to 25x, implying a target price of US$450.

☀ External demand and ASP upside drive TPU forecast revision:
We now expect Google TPU shipments to reach 4.5 million units in 2026 and 7.9 million units in 2027, versus our previous forecast of 4.5 million and 6.0 million, mainly due to stronger demand from external customers. Of these, TPU shipments designed by Broadcom are expected to total 4.1 million units in 2026 and 5.8 million units in 2027. On October 23, 2025, Anthropic announced a major expansion in TPU usage, with contract value reaching tens of billions of dollars, and this is expected to contribute more than 1GW of compute capacity in 2026. We believe Broadcom will be the key beneficiary of this wave of external demand, as its Ironwood and Sunfish products are already available for customer testing, while MediaTek’s Zebrafish has not yet begun. On ASP, we expect the uptrend to continue. Pumafish, scheduled for launch in 2027, features greater design complexity and could carry an ASP of more than US$20,000. In comparison, our shipment forecasts for Meta ASICs have been lowered to 300,000 units in 2026 and 500,000 units in 2027.

☀ AI networking business continues to support growth:
According to the latest earnings results, AI networking is becoming an increasingly important growth driver alongside compute. In 1Q, AI networking revenue grew by approximately 60% YoY, accounting for about one-third of total AI revenue. Management expects that share to rise to around 40% in 2Q, and to remain within the 33%–40% range over the longer term. Looking ahead, Tomahawk 7 is expected to launch in 2027, with performance improving by roughly 2x. On architecture, Broadcom reiterated that copper interconnect technology in scale-up systems can support the transition from 200G to 400G SerDes through 2028. The TH series, used in scale-up products for Meta and AMD, is expected to begin contributing revenue in 2026. As such, the company views CPO as a long-term evolution path rather than a near-term necessity. We forecast shipments of its scale-up CPO switches to reach 5,000 units in 2026 and 20,000 units in 2027.

$AVGO


I'm worried about LG Electronics. I'm genuinely very concerned.

Summary: Bearish LG Electronics into 2026: legacy memory price surge will hammer HA costs, demand may weaken more than laptops.

In 2026, one group of companies that could face major headwinds would be HA (home appliance) companies.

Home appliances tend to use older-generation memory, and since the prices of legacy memory have surged sharply, it looks like this will hit costs hard.

It might even be that HA ends up suffering more from demand weakness caused by steep price hikes than laptops do.

I’m worried about LG Electronics. I’m genuinely very concerned.

---

https://t.co/Y55d048eyk


Summary: Endorses GFHK PT raise on UMC; bullish on specialty process positioning, long-term growth, and silicon photonics order wins.

GFHK has raised its price target on UMC.

They noted that UMC should benefit as TSMC scales back legacy capacity.

What we and GFHK agree on is that UMC has a very solid positioning in specialty processes. Its long-term growth potential also looks compelling. We believe UMC has a meaningful chance of winning key silicon photonics orders.

For more details, please refer to the link below.

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https://t.co/TKa2Emhunf


When NVIDIA's ambitions extend beyond the GPU toward system-level optimization of the entire agentic workflow, the difficulty of competing is no longer on any single dimension... NVIDIA is operating from a systems perspective — orchestrating heterogeneous hardware across the entire agentic flow from the ground up. In terms of system complexity and software stack depth (Dynamo / ICMS / CMX), this move raises the competitive bar from 'build a great chip' to 'define an entire heterogeneous system as a universal accelerated computing solution.'

Summary: Long NVDA: post-Groq heterogeneous inference stack (LPU+CPX+HBM GPU+ICMS) raises competitive bar to system-level, mirroring mobile SoC consolidation winners.

GTC 2026 Preview: The New Era of Heterogeneous AI Inference Through the Lens of Groq's Niche

Where does Groq's SRAM approach fit in the ecosystem? Will SRAM displace HBM? How will NVIDIA integrate Groq into its existing product lineup — technical integration or product line integration? And what upgrades will the acquisition bring to the Groq LPU?

Here I attempt to piece together a logical chain from first principles.
---------------------------------
Groq's Design Philosophy: Compiler-First, Not SRAM-First

Groq is fundamentally a compiler-first architecture taken to its extreme — SRAM is a byproduct, not the primary thesis.

Unlike CPUs designed for general-purpose workloads, AI inference is characterized by high determinism: virtually no data-dependent branching, fixed tensor shapes, and predictable memory access patterns.

When Groq examined the hardware-software interface through this lens and asked "what should be done at compile time versus runtime?", the answer for AI inference was: almost everything can be done at compile time.

This is Groq's most radical and distinctive insight: a fully deterministic compiler, scheduled down to the exact clock cycle. Complete determinism enables extreme efficiency. To achieve this, the compiler must account for every state of the hardware at every moment of execution — acting as an omniscient god — which eliminates wasted hardware resources. This requires every computation, memory access, and communication latency within the LPU to be precise to the clock cycle, which is enormously complex for a compiler.

The high determinism of AI workloads, combined with Groq's fully deterministic compiler approach, naturally avoids VLIW's weaknesses (unpredictable memory and branch behavior) while amplifying its strengths. The next step — increasing efficiency and parallelism — naturally leads to VLIW-style instruction encoding: since the compiler controls what each functional unit does every cycle, packing multiple operations into a single wide instruction yields higher efficiency. This is VLIW.

In Groq's chips, there is no out-of-order execution or speculation. Hardware is dramatically simplified (instruction dispatch occupies less than 3% of die area), and complexity is shifted entirely to the static compiler. This is the essence of the VLIW philosophy.

To enable cycle-accurate deterministic scheduling by the compiler, all sources of non-determinism in hardware must be eliminated — arbiters, crossbars, replays, and any component that makes runtime decisions via autonomous algorithms are removed.

Memory latency must also be deterministic, which is why all caches and DRAM are eliminated. Caches are replaced with scratchpad SRAM, because cache replacement policies are runtime decisions — non-deterministic. Addresses are fully controlled by the compiler, guaranteeing determinism.

Communication must also be cycle-precise. Send and receive instructions are executed at compiler-scheduled moments — there are no traditional "I want to send a packet, please allocate memory" operations. Instead, SRAM allocation and data transfer are executed synchronously according to a pre-determined timetable decided by the compiler; the hardware simply follows it.

The fully deterministic compiler also delivers extremely low overhead latency for inter-chip communication — arguably Groq's most underappreciated advantage. In traditional interconnect architectures, packet routing, arbiter contention, and buffer queuing are the primary sources of latency variance.

So Groq is not fundamentally an SRAM-first architecture, nor strictly a VLIW-first architecture — it is a compiler-first architecture. More precisely, the fully deterministic compiler is the core of the entire Groq design.

SRAM-only became a necessary consequence of the deterministic compiler: at the core decode stage, the non-determinism inherent to HBM/DRAM is incompatible. This is why Groq is better described as compiler-first.
------------------------------------
Can Groq's Deterministic Compiler Be Applied to NVIDIA's GPU + HBM Architecture?

No. For two reasons:

1. HBM/DRAM's physical characteristics make deterministic latency impossible.

- DRAM cells must be refreshed at regular intervals (tREFI) to replenish charge, blocking bank access. This is dictated by DRAM cell physics, and refresh frequency varies with temperature.

- To maximize DRAM bandwidth, controllers perform dynamic optimizations such as batch scheduling — grouping traffic to the same page to minimize page misses, spreading access across banks, and minimizing read-write switching. These optimizations occur in real time and are fundamentally unpredictable.

- System-level DRAM optimizations like bank address hashing make it extremely difficult for a compiler to statically pre-locate specific data, and the complexity of enforcing cycle-level determinism becomes prohibitive.

These non-deterministic behaviors can technically be resolved — but only by abandoning most DRAM optimization strategies, severely degrading efficiency and utilization. Groq itself explored this direction and filed a patent for deterministic DRAM, but concluded that practical implementation was infeasible. This is one of the core reasons Groq chose SRAM-only.

Applying a deterministic compiler to DRAM is not a yes-or-no question — it's simply a bad choice, because it structurally and unavoidably sacrifices HBM efficiency and bandwidth. It would essentially require rewriting a full memory controller as a compiler-defined software construct — extraordinarily complex, requiring major updates with each memory generation, and a validation nightmare across every DRAM vendor and generation.

2. NVIDIA's SIMT architecture is philosophically opposed to Groq's compiler-first approach.

These two architectures give opposite answers to the same problem — runtime non-determinism:

- Groq eliminates all non-determinism at compile time.

- NVIDIA hides unpredictable latency through warp switching.

NVIDIA GPUs are built on SIMT (Single Instruction, Multiple Threads) and hardware warp schedulers. When a warp stalls on a memory access, the scheduler instantly switches to another ready warp, hiding the stall latency. This entire mechanism presupposes that latency is unpredictable, so sufficient concurrent threads are needed to statistically fill the pipeline.
Replacing this with a deterministic compiler would mean abandoning NVIDIA's most fundamental hardware scheduling unit — and with it, the need for large register files to support multi-warp rotation.

Historically, AMD's transition from TeraScale (VLIW) to GCN (scalar SIMT) was precisely a large-scale VLIW-to-SIMT migration: when workloads became less predictable, the compiler burden of VLIW became too heavy, and scheduling authority had to be returned to hardware.

Groq's only viable path within NVIDIA is as a standalone, purpose-built product for low-latency decode.
--------------------------------------------------
What Will NVIDIA Bring to Groq?

Groq's current bottlenecks, simply stated:

1. SRAM capacity is too small to hold large model weights + KV cache

2. The primary decode bottleneck is not SRAM bandwidth (80 TB/s) but interconnect latency (accounting for ~80% of total latency)

3. Prefill (a compute-bound task) is relatively slow

Groq's core architecture was essentially finalized in 2017–2018, the era of CNNs. The architecture targeted CNN/LSTM workloads, with benchmarks like ResNet50, for which 230MB of SRAM per TSP compute card was more than sufficient.

But in the LLM era, 230MB of on-chip SRAM per card is woefully inadequate. A LLaMA 70B model's parameter footprint is equivalent to ~3,000 ResNet50 models, and the KV cache grows further with long context. Scale-out became the only option.

Running a 70B model thus requires a 576-card cluster: 16-stage pipeline parallelism (PP) × 36-way tensor parallelism (TP), with 80 transformer layers split into 16 pipeline stages and each stage spread across 36 cards. The PP and TP communication overhead constitutes over 80% of total latency — PP alone accounts for more than 50%.

After joining NVIDIA's product line, Groq can play to its strengths by focusing exclusively on decode (where it excels) and avoiding prefill (where it is weak).

The most important improvement NVIDIA can bring is likely expanding Groq LPU SRAM capacity through process node upgrades and hybrid bonding technology (analogous to AMD's 3D V-Cache). Moving from 14nm to 3nm alone could grow SRAM from 230MB to ~500MB per chip; adding 3D SRAM stacking could double capacity again.

With larger SRAM, a 70B inference job that previously required 576 LPUs might need only 256. With 32-way tensor parallel × 8-stage pipeline parallel, pipeline interconnect latency could be cut roughly in half.

NVIDIA's primary contribution: expand SRAM capacity → reduce scale-out card count → reduce communication latency → increase token throughput.
---------------------------------------------
Will SRAM Displace HBM?

No.

SRAM is fundamentally an exchange of 10× the cost for a few times the speed. It is viable only for a subset of customers willing to pay a premium for low latency. The dominant metric in the AI hardware market remains TCO (total cost of ownership).

A simple cost comparison makes this clear: running LLaMA 70B including KV cache requires 576 Groq cards. At a retail price of ~$20,000 per card (the CEO has stated actual prices are far lower, so let's use $2,000), that's over $1.1M in hardware costs. Two H100s can run the same model for under $100,000 — a 10× cost gap.

Groq pivoted to selling token API services, and its pricing is indeed cheap — but for two reasons: (1) NVIDIA GPU cloud providers typically sell capacity at 2× hardware cost; and (2) Groq itself is operating at a loss. In 2025, Groq's LPU-based API inference business generated ~$40M in revenue against ~$60M in costs — a –50% gross margin. Groq's low token prices are not evidence of SRAM's economic superiority; they are VC subsidies.

Is there a market willing to pay a speed premium? Yes.

Claude Opus 4.6's Fast mode is a telling market signal: 2.5× faster output, priced at $30/$150 per million tokens versus $5/$25 — a 6× price increase, presumably achieved by sacrificing batch efficiency. This market segment is real, and SRAM has a genuine niche here.

But how large is this niche?

Different workloads have vastly different hardware requirements:

Prefill: low bandwidth requirement, high compute requirement

Decode: the reverse — high bandwidth, lower compute

Ranking & Recommendation: modest compute and bandwidth, but enormous capacity requirements

Latency-sensitive decode (the red zone in the accompanying chart) is SRAM's domain: real-time, interactive LLM applications — chat, copilot, agents. Reasoning models in particular show dramatic improvements: H100 might take 2–3 minutes to complete a reasoning chain; Cerebras handles it in 10 seconds.

The share of inference workloads demanding extreme speed appears to be around 10% of hyperscaler compute, based on one data point.

Agentic workloads are more complicated: profiling common agentic frameworks (SWE-Agent, LangChain, Toolformer) shows CPU can account for up to 90% of end-to-end latency, with throughput bottlenecks more often in CPU than in decode. The SRAM speed advantage is substantially diluted here.

The larger volume workloads — batch inference, offline processing, ranking, recommendation — are insensitive to latency; throughput and cost-per-token are the only metrics. SRAM has no cost advantage here whatsoever.

An analogy: H100/B200 is a bus — high passenger capacity (large batches), low per-seat cost, but slower. Groq/Cerebras is a Ferrari — extreme speed, few passengers, per-seat cost 10× or more.

Structurally, SRAM's cost disadvantage will not converge over time. A 6T SRAM cell is inherently more expensive than a 1T1C DRAM cell — this is determined by physics, not process node. And SRAM scaling has stalled: from N5 to N3E, SRAM cell area has barely shrunk.

Even the speed advantage is eroding: SRAM access speeds are approaching process limits with little room for generational improvement, while HBM bandwidth is scaling exponentially. A decade ago, SRAM's 2-order-of-magnitude speed lead over HBM was a crushing advantage. Today the gap is less than one order of magnitude (Rubin HBM4: 22 TB/s). In another decade, the gap may be negligible.

Conclusion: SRAM will not displace HBM, but it has an irreplaceable role in the low-latency, low-batch, real-time inference segment. Long-term, as HBM bandwidth scales exponentially, even that advantage will gradually diminish.
-------------------------------------------------------
The New Era of Heterogeneous Inference

Piecing these fragments together, the outline of NVIDIA's post-Groq acquisition strategy comes into focus: the dawn of heterogeneous inference.

Inference workloads have already diverged — no single architecture can cover the optimal operating point for all of them. The most important principle in computer architecture is the tradeoff and the range of scales it covers. A single architecture may be extraordinary within its optimal tradeoff space; using multiple architectures tailored to different workload types is the essence of heterogeneous computing.

The defining theme of GTC 2026 is the systematization of heterogeneous inference. Inference will no longer be handled by a single piece of hardware — it will be decomposed:

- Control, scheduling, and agent runtime → Vera CPU

- Long-context prefill → CPX (Content Phase Accelerator, a compute-bound prefill-optimized module)

- Small models / low-latency / low-batch decode → Groq LPU (256-card clusters)

- High-throughput / high-concurrency batch decode → HBM GPU (still the workhorse)

- ICMS / CMX (Inference Context Memory Storage): KV cache is now core infrastructure. Heterogeneity has extended beyond compute into the memory hierarchy itself

LPU and GPU will likely become two different tiers within the inference stack: small models, low-latency, low-batch workloads to LPU; long context, high-batch workloads to HBM GPU.

How CPX connects to LPU/GPU remains unclear. The general workflow would be: CPU handles control and scheduling → CPX completes prefill and generates tens of GB of KV cache → distributed to Groq LPU SRAM arrays or HBM GPU → decode begins.

There is also a more speculative possibility: speculative decoding, where LPU runs the typically smaller draft model at blazing speed and HBM GPU serves as the verifier. This heterogeneous decoding structure could substantially increase token rates — potentially doubling them in some scenarios (e.g., coding tasks, where syntax is predictable and small draft models achieve high acceptance rates).

When NVIDIA's ambitions extend beyond the GPU toward system-level optimization of the entire agentic workflow, the difficulty of competing is no longer on any single dimension. NVIDIA used to take big leaps through brute-force advances in GPU architecture and parameters. Now, with CPX, LPU, and ICMS joining the heterogeneous inference stack, NVIDIA is operating from a systems perspective — orchestrating heterogeneous hardware across the entire agentic flow from the ground up.

In terms of system complexity and software stack depth (Dynamo / ICMS / CMX), this move raises the competitive bar from "build a great chip" to "define an entire heterogeneous system as a universal accelerated computing solution."
---------------------------------------------------------
A Historical Parallel

Every paradigm shift in computing has spawned a wave of semiconductor startups. But as software and application forms converge, large incumbents ultimately absorb the functionality through acquisitions — scaling parameters higher, integrating systems more deeply and comprehensively, reducing costs, improving power efficiency and benchmark scores — gradually crowding out independent startups.

In the early mobile era, there was a profusion of independent companies building application processors, discrete baseband chips, ISPs, and GPUs. The ultimate winners were those who integrated GPU, ISP, and modem into a single SoC with complete system-level heterogeneous compute platforms.

Apple acquired PA Semi's CPU team, Infineon's modem, and hollowed out Imagination's GPU. Qualcomm acquired ATI's mobile GPU, Atheros for Wi-Fi, Nuvia for CPU, CSR for Bluetooth/DSP — all canonical examples.

As heterogeneous inference grows in complexity, companies capable of system-level integration will have an increasing advantage. The logic is identical to the mobile SoC era. In the AI era, NVIDIA's acquisitions of Arm (failed), Mellanox, and Groq are merely the beginning of this new historical cycle.

$NVDA

GTC 2026 preview： 从Groq生态位看AI异构推理(Heterogeneous Inference)新时代

Groq的SRAM路线的生态位在哪里？SRAM会不会替代HBM路线？

Nvidia如何整合groq到现有的产品线？是技术整合还是产品线整合？收购之后会给groq LPU产品带来怎样的升级？

这里尝试从基本原理出发去拼凑一个逻辑链 https://t.co/oAS2suk2uo


We believe this is a correction, not a peak-out... riding through this correction offers a more favorable risk-return profile

Summary: Samsung Securities analyst calls current selloff in Samsung Electronics and SK Hynix a correction not a peak; recommends riding through it as DRAM cycle plateaus rather than peaks out.

Views on Memory Stock Price Decline
[Samsung Securities Semiconductor, IT / Jong-wook Lee]

As of 10:30 AM, Samsung Electronics and SK Hynix shares are down 3.9% and 5.6%, respectively.

1. Spot Price Decline

D5 16G spot prices peaked on March 19 and have since corrected over the past week, with the premium over contract prices narrowing from a 1Q average of 30% to roughly 20% recently. The equity market is concerned this may mark an inflection point signaling DRAM price declines.

We believe the pace of DRAM price increases will begin to moderate from 3Q onward, and that early signs of this deceleration could trigger a share price correction around May–June. From this same perspective, the narrowing of spot price premiums appears to be interpreted as evidence of slowing DRAM price momentum.

2. Views on the Correction

We believe this is a correction, not a peak-out. The market narrative will shift from the magnitude of DRAM price increases to the sustainability of DRAM prices. Because it is difficult to pinpoint the exact inflection between these two themes, we believe riding through this correction offers a more favorable risk-return profile.

We see a high probability that DRAM profits will trace a broad plateau rather than the typical cyclical peak, as the supply-side response has lagged roughly one year behind compared to previous cycles. We believe this plateau-shaped earnings profile will instill greater confidence among investors in the sustainability of the cycle.

3. The Cycle

We maintain that DRAM remains a cyclical industry. That said, we caution against selling on the vague fear that "it's cyclical, so it has to come down eventually."

Cyclical peak-outs originate from changes in downstream industries. Therefore, early signs of a peak-out can be identified through business trajectory shifts at OpenAI, Anthropic, and the ISPs — and we believe there is still upside to be demonstrated from the demand side.

Looking back at 2025, the equity market has wobbled every time spot prices stalled. While spot price declines can induce fear among investors, we do not believe they should be extrapolated as evidence of a cyclical peak-out.

4. Natural Gas Supply Issues

Natural gas is a cost inflation issue, not a production disruption issue. In memory, cost is dominated by the variability of technology, yields, and capex — which has historically overwhelmed raw material cost fluctuations. Moreover, in a commodity industry, raw material supply disruptions cannot be viewed as purely negative. What matters is whether demand is strong enough to absorb cost pass-throughs.

5. Sentiment

Examining the drivers behind the recent share price correction, this is a sentiment issue rather than a fundamental one — though sentiment itself is a meaningful factor in equity markets. Investors are feeling significant anxiety about sustainability following the sharp run-up in both DRAM prices and stock prices, and they want concrete evidence to alleviate that anxiety.

If tangible evidence emerges regarding the direction from 3Q onward, we believe share prices can stabilize and begin to reflect earnings with greater confidence.

Thank you.

(Published 2026/3/30)


riding through this correction offers a more favorable risk-return profile


I picked up a small amount of Ibiden shares today. I agreed with Bernstein's view that even if Intel wins EMIB-T orders from external customers, it's actually Ibiden—the one supplying the substrates to Intel—that stands to benefit far more. On top of that, I believe ABF substrates are going to face severe shortages going forward.

Summary: Long Ibiden: ABF substrate supplier benefits most from Intel EMIB-T wins; 42% supply shortage projected over next two years.

I picked up a small amount of Ibiden shares today.

I agreed with Bernstein’s view that even if Intel wins EMIB-T orders from external customers, it’s actually Ibiden—the one supplying the substrates to Intel—that stands to benefit far more. On top of that, I believe ABF substrates are going to face severe shortages going forward.

And coincidentally, Goldman Sachs just put out a report saying that ABF substrate supply is projected to fall short by a whopping 42% over the next two years.

I’m really disappointed that I suddenly ran short on cash and couldn’t buy more Ibiden.


Summary: Terse buy instruction on Intel.


Summary: Don't sell MU on HBM4 supply news; server DDR5 margins compensate and the selloff is unwarranted.

Wasn’t it already widely known news that Micron wouldn’t be able to supply HBM4? I don’t understand why everyone is making such a big fuss about it.

And selling Micron because of this is foolish, too. Micron can earn even higher margins from server DDR5 instead of HBM4.


Summary: Bullish AAPL on growing smartphone market share gains in India and resilience vs Samsung in China.

Is it believable that Apple has already broken out of the “Others” category and climbed to 5th place in the Indian market?

Unlike Samsung, Apple didn’t collapse in China, and it has successfully established itself in India.

At this point, it feels like Apple only has upside left in smartphone market share going forward.

$AAPL


I invest in Intel based more on the U.S. government backing Intel than on Intel's own competitiveness

Summary: Long INTC premised on U.S. government backing rather than Intel's own competitive position.

@sidsomanii I invest in Intel based more on the U.S. government backing Intel than on Intel’s own competitiveness.

@jukan05 Does this play into your INTC thesis?


Keyware has also stated that demand for high-end drill bits exceeds supply, and is pursuing capacity expansion... order visibility extends through Q3 2026

Summary: Bullish Topoint and Keyware as AI-driven PCB drill bit demand surges, supply tightens, and both companies expand capacity with visible order books into 2026.

Keyware Taiwan PCB drill bit maker 6152.TW

PCB Boom Triggers Drill Bit Shortage.. Topoint & Keyware Begin Capacity Expansion — Once a Consumable, Now a Major AI Business Opportunity

- Rising demand for AI servers is triggering bottlenecks in PCB materials. MLB designs for NVIDIA's GPU and ASIC platforms have expanded to 40+ layers, with growing adoption of ultra-low-loss CCL specifications such as M7 and M8.

- As a result, the processing difficulty of advanced substrates has surged dramatically, elevating drill bits — once considered mere "consumables" — into critical strategic materials.

- To address capacity shortfalls, Taiwan's Topoint and Keyware have begun expansion efforts, actively responding to growing AI demand.

- According to industry sources, high-end CCL (copper clad laminate) has high hardness, which drastically shortens drill bit lifespan.

- Drill bits that previously lasted ~3,000 hits per unit have now dropped to under 800, and with the potential introduction of M9-grade materials, usage could fall to as few as 100 hits.

- Processing methods are also shifting from "1 hole, 1 bit" to "1 hole, 3–5 bits." To manage yields, some PCB manufacturers have banned the re-grinding and reuse of worn bits, causing overall consumption to surge.

- In light of tight supply conditions, Topoint has begun expanding capacity with a focus on high-end products. The company recently invested approximately NT$560 million to secure a second factory in Zhongli District, Taoyuan, primarily for mass production of premium coated bits.

- The facility is located near major PCB manufacturers including Kinsus, GoldCircuit, and Compeq. Equipment installation will begin this year, with full-scale production expansion expected from next year.

- Keyware has also stated that demand for high-end drill bits exceeds supply, and is pursuing capacity expansion.

- Keyware plans to expand production capacity to 15 million units/month (vs. Topoint's 35 million units/month) by the end of Q1 2026, and emphasized that its order visibility extends through Q3 2026.

- The growth rate of drill bit demand driven by AI advanced substrates significantly outpaces the overall PCB industry growth rate. As capacity is being rapidly depleted, early signs of price increases have also been detected.

- Price hikes are likely to begin with lower-end "white bits," but if specifications are further upgraded alongside new platform mass production in 2026, price increases could extend across premium products more broadly.

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https://t.co/h4suO4ryY9


Summary: UBS reiterates Buy on SK Hynix, raises PT to Won1m on 82% DRAM OPM forecast by 4Q26, DDR upcycle, and HBM leadership; 2026E OP 52% above consensus.

UBS: SK Hynix

Significant profit uplift potential - PT to Won1m

We forecast 82% DRAM OPM by 4Q26
The memory semis industry is in the midst of an unprecedented upcycle. By 4Q26, we now forecast SK Hynix DDR ASPs to be a whisker away from the 3Q18 peak at US$0.97 per Gb. With an upcycle of that magnitude, we believe conventional memory, especially DDR (52% of 4Q25E revs.), will likely be the key profit driver in 2026 and 2027. We forecast DDR operating profit to increase 2.9x YoY in 2026 and account for 67% of total, with operating margin reaching 82% by 4Q26. We also expect SK Hynix to continue to strongly benefit from AI compute semis growth, due to its HBM leadership position (UBSe 56% bit market share in 2026). We note that in its recently published "2026 Market Outlook" (5 Jan 2026; link), SK Hynix referenced UBS research assessing that it will ship first to Google for TPU7e/7p, and will likely secure c70% share for HBM4 at Nvidia (Rubin) in 2026. All in all, we forecast SK Hynix to generate Won75trn in FCF in 2026, and Won98trn in 2027. We forecast its book value per share to grow 104% YoY from end 2025 to 2027. At 2.23x NTM (to March 2027E) book, SK Hynix shares should continue to re-rate to reflect longer-term economics which have fundamentally shifted.

Strong uplift to DDR and NAND contract pricing in 1Q26E and beyond
Our industry checks indicate very strong contract pricing for 1Q26E, especially DDR. We forecast SK Hynix DDR contract pricing to rise 60% QoQ in 1Q26 (was +30%), with server DDR up 65%, and Mobile DDR up 60%. We forecast NAND contract ASPs up 27% QoQ. Our checks also show strong confidence in positive momentum carrying into 2Q26E and beyond, in particular for DDR. We continue to forecast DDR contract pricing to increase til 1Q27, and NAND til 3Q26. This leads us to forecast SK Hynix DRAM operating margin to reach 77% by 4Q26, well above the prior peak of 65% in 3Q18.

Significantly upping estimates, increasing 2026E capex
We increase our 4Q25E OP to Won18.2trn, 12% above VA consensus of Won16.2trn, on the back of higher DRAM ASPs (+21% QoQ vs. 20% prior), NAND (+30% vs. +20%), and NAND bit shipments (+11% vs. +4%). We increase our 2026E OP by 211% to Won150.2trn, 52% above consensus of Won99trn, and 2027E by 19% to Won157.8trn, 29% above consensus of Won122.3trn. These changes lead us to raise 2026E/27E EPS by 21%, on the back of raised DDR and NAND flash contract pricing forecasts. We also increase our 2026 capex forecast to Won38trn from Won35trn.

Valuation: Raise PT from Won853,000 to Won1,000,000 - Key Call Buy
We value SK Hynix shares at 3.00x NTM (to March 2027E) book (was 2.99x) based on our 2026-30 LT ROE forecast of 34.3% (was 33.7%) and CoE of 11.5% (was 11.3%). Our revised price target also reflects our increased book value per share forecast.


Summary: Bearish MU: seen as effectively disqualified from Rubin HBM4, trailing Samsung and Hynix.

SA: Micron is well behind Samsung and Hynix and we believe they are effectively out of the picture for Rubin HBM4. We have more details on qualifications and pin speeds in the Accelerator and HBM model. https://t.co/2ysVLO4DUV

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https://t.co/HxVTfbV1ao


Summary: Bullish HK/China tech (Tencent, Alibaba, HST Index) as capital rotation from Korea nears peak; bearish near-term on Samsung/Hynix on overbought/crowded signals.

>> Is Korea to Blame for the Hang Seng Tech Index's Underperformance? (Haitong International Securities Strategy)

- Many investors are now concerned that the Hong Kong market may be re-entering a bear market. Looking at recent trends, however, it's less that Hong Kong has underperformed on its own merits and more that other markets have simply been stronger. Since the start of 2025, foreign capital within emerging markets has been flowing back into Chinese assets, benefiting both A-shares and Hong Kong equities — with particularly strong foreign inflows into the Hong Kong market.
However, since February, capital has shifted en masse out of the Hong Kong market and the Hang Seng Tech Index into Japan, Korea, and Taiwan — with a notably outsized tilt toward Korea. That said, Korea's market strength has essentially been concentrated in just two names: Samsung Electronics and SK hynix. On the surface, the Korean market as a whole appears strong, but in reality it's closer to a "two-man show," with the rest of the market largely being dragged up passively.

- So when could this capital dispersion trend start to ease? First, there's a possibility that the current phase of Japanese, Korean, and Taiwanese markets absorbing Hong Kong's capital flows is entering its later stages. Korea's market is flashing strong short-term overbought signals, and positioning has become quite crowded. While the market's high retail investor participation drove strong upside momentum, sentiment now appears to be near a short-term peak — suggesting the "K-wave" shock could begin to moderate.

- On the other hand, looking at the Hong Kong market, discussions with multiple hedge funds regarding core Hang Seng Tech heavyweights like Tencent and Alibaba suggest that the current environment is one where it's difficult to generate additional returns from the short side, given these names have already seen significant declines over recent months. Tencent is currently trading at only low-teens P/E. Alibaba still possesses one of China's top-tier AI large language models, giving it genuine technology momentum — yet the market's focus remains fixated solely on traditional economy and domestic consumption recovery narratives.
$BABA

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https://t.co/QgP7mTP3K9


Tencent is currently trading at only low-teens P/E

industry='Internet Content & Information'


Alibaba still possesses one of China's top-tier AI large language models, giving it genuine technology momentum


Korea's market is flashing strong short-term overbought signals, and positioning has become quite crowded


Summary: Shares TrendForce's sharply upward-revised DRAM and NAND price forecasts for Q1/Q2 2026, implying bullish memory pricing outlook.

DRAM sector basket: Micron, Samsung, SK Hynix

TrendForce Raises Q1/Q2 2026 DRAM & NAND Price Forecasts

Q1 2026

- PC DRAM Blended ASP: revised up from +50~55% to +110~115%
- Server DRAM Blended ASP: revised up from +60~65% to +93~98%
- Mobile DRAM Blended ASP: revised up from +45~50% to +58~63%
- Overall DRAM Blended ASP: revised up from +55~60% to +93~98%

- eSSD prices: revised up from +33~38% to +75~80%
TLC/QLC NAND prices: revised up from +30~35% to +75~80%
- Overall NAND Blended ASP: revised up from +33~38% to +85~90%

Q2 2026

- PC DRAM prices: revised up from +10~15% to +40~45%
- Server DRAM prices: revised up from +10~15% to +43~48%
- Mobile DRAM (LP5X) prices: revised up from +13~18% to +58~63%

- eSSD prices: revised up from +15~20% to +68~73%
- TLC/QLC NAND prices: revised up from +15~20% to +60~65%
- Overall NAND Blended ASP: revised up from +18~23% to +70~75%


Summary: Bernstein sees AI infrastructure/memory cycle intact for several quarters; names SNDK, STX, WDC, Dell, HPE, SMCI as structural beneficiaries.

Bernstein: Even if the scaling laws break down, hyperscalers’ capex is unlikely to collapse abruptly and would likely remain resilient for several quarters

The intelligence revolution has substantial room to run as ongoing model improvements unlock new use cases and boost productivity, implying an estimated $1.3T in enterprise willingness to pay for AI in our base case. These improvements drive both training and inference needs, structurally benefiting AI infrastructure such as servers and memory. We believe that the most watched signpost - hyperscaler capex - may be more of a lagging indicator, while consistent model improvement should be a better sign post. We see near-to-medium term data points as healthy since models keep improving, and further note that if a digestion cycle happens, there is likely time to take profits before it fully unwinds.

We believe the intelligence revolution still has substantial room to run, driven by continued model improvement that expands the set of AI-addressable tasks. Using the Anthropic Economic Index, we estimate a $16T global productivity opportunity, assume that enterprises demand a 10x ROI requirement for IT projects (in line with historical precedents), and assume 80% user penetration, which leads to a $1.3T enterprise AI willingness to pay in the base case (Exhibit 1-Exhibit 3). Importantly, this willingness to pay rises as models improve: better models address more complex tasks, unlock incremental productivity gains, justify continued investment in both training and inference, and expands willingness to pay. Based on the OSWorld Agentic Task Benchmark, we can see that model performance has been getting closer to the human benchmark (Exhibit 4), which we see as a positive leading indicator. While inference is where monetization ultimately occurs, training is strategically essential, since ongoing model improvements are required to expand addressable use cases and sustain future demand for inference and AI infrastructure.

Even if a digestion happens, there is likely still time to reduce exposure before things fully unwind. If the scaling laws truly started to break down, the early indicator would be that AI labs start training new models which end up not being better than the smaller models of the previous generation. However, AI labs are unlikely to immediately publish those results. More likely, they would attempt to fix the issues by re-attempting a different run, which could take months. Even when it becomes clear that something is wrong, AI labs will be left guessing whether this is solely their own problem or an industry-wide breakdown in the scaling laws¹. Word would likely get out gradually, through a trickle of researchers exiting or complaining to the press². It will likely take months before it becomes clear that it is an industry-wide problem. Even then, VCs and hyperscalers will appear less concerned about this risk, in part because hyperscalers can redeploy excess GPUs to other workloads while VCs appear willing to accept these risks. Therefore, they are likely to be slow to react, and GPU-buying can continue for some time. On net, we believe it would likely be at least a year before it is clear that it is a problem, and hyperscaler capex would likely continue to be healthy for a few quarters after.

We view the Intelligence Revolution as structurally positive for AI server and memory vendors. As model size and complexity continue to expand, larger workloads and the growing need for nearline storage will further drive the demand for memory, which is positive for vendors like SNDK, STX, and WDC. Moreover, the expansion of AI use cases is driving growth in both training and inference over time, supporting the sustained demand for AI infrastructure including AI servers, supporting names like Dell, HPE, and SMCI.


supporting names like Dell, HPE, and SMCI


The market is way too optimistic about MediaTek's TPU volume. MediaTek's smartphone business is collapsing amid brutal price competition with Qualcomm and others.

Summary: Short MediaTek on collapsing smartphone biz + TPU volume over-optimism; long Broadcom as essential to Google's NVIDIA-alternative ambitions.

1.  The market is way too optimistic about MediaTek’s TPU volume.

2.  Google is pushing ahead with a fully in-house, completely CoT-designed 2nm TPU project, completely excluding both Broadcom and MediaTek. (WTF?)

3.  Google wants to become a real alternative to NVIDIA, and for that, Broadcom is absolutely essential.

4.  MediaTek’s smartphone business is collapsing amid brutal price competition with Qualcomm and others.

Gokul just kicked MediaTek’s ass again. https://t.co/eU0VnUvhXq


Google wants to become a real alternative to NVIDIA, and for that, Broadcom is absolutely essential.


Institutions believe this round of price increases will positively contribute to TSMC's 1Q26 results... TSMC's performance during what is traditionally a seasonal slow period could remain in line with last year or even post slight growth

Summary: Shares report of TSMC consecutive annual price hikes 2026-2029 on tight advanced-node supply; institutions see positive 1Q26 contribution and resilient performance.

TSMC to Raise Prices Again for Advanced Nodes at 3nm and Below Starting January 2026
(Taiwan Economic Daily)

• As global AI demand continues to surge, TSMC’s production capacity for advanced processes at 3nm and below remains tight, with supply still unable to catch up with demand. Accordingly, TSMC is reportedly coordinating with customers to implement consecutive annual price hikes for advanced nodes for four years, from January 1, 2026 through 2029.

• Institutions believe this round of price increases will positively contribute to TSMC’s 1Q26 results. In particular, with major AI-chip customers such as Nvidia and AMD continuing to roll out new products, TSMC’s performance during what is traditionally a seasonal slow period could remain in line with last year or even post slight growth.

• Industry estimates suggest the 2026 price increase will “vary by process,” but will generally fall within a range of roughly 3% to 10%. The actual increase applied will differ depending on each customer’s purchase volume and bargaining power.

• While TSMC has not issued an official statement on its pricing policy, the report emphasizes that, amid supply shortages and expanding AI demand, customers are still actively seeking to secure advanced-node capacity.

$TSM

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https://t.co/aLY6PYwxDX


Summary: Shares Evercore bullish channel check: Vera Rubin pulled forward 3-6 months, shipments possibly starting end of 2Q26.

Evercore: Vera Rubin timeline pulled forward!!

Some sources believe that China export bans have enabled NVDA to leverage suppliers that previously served China-bound volumes for global product development, pulling Rubin's schedule forward by 3-6 months.

Some would even not be surprised if Rubin shipments begin by end of 2Q26.

$NVDA

Evercore out with a 4Q25 AI Channel Checks, saying $NVDA Vera Rubin appears to be ahead of schedule https://t.co/CqWOUnl5Q8


Summary: Bullish Anritsu as a standout Japanese player in optics, flagged as especially intriguing for sector investment.

industry='Scientific & Technical Instruments'

People often say that optics will be the next big money-making field after memory, right?

This is an interesting article introducing noteworthy Japanese players in optics that you should pay attention to when considering investments in the sector.

Among the companies featured here, Anritsu (6754T) in particular caught my eye as especially intriguing.


Summary: GF Securities reiterates Buy on MU, raises PT to $623; confident in DRAM upcycle extending through 1H27, strong HBM ramp, and rising ASPs.

GF Securities Overseas Electronics & Communications | Micron FY2Q26 Review: No Need to Worry About the CAPEX Increase

☀ Results significantly beat expectations; target price raised to US$623: Micron delivered a very strong performance in fiscal 2Q26, with revenue reaching US$23.9 billion and non-GAAP EPS of US$12.20.

☀ Fiscal 3Q26 guidance came in well above expectations: midpoint guidance is US$33.5 billion in revenue and US$19.15 in EPS (vs. around US$15 in buy-side checks and GF’s forecast of US$19.78).

☀ Micron also announced that it began volume shipments of fourth-generation HBM to NVIDIA in calendar 1Q26, with yield maturation progressing faster than for HBM3E.

☀ The stock fell 4% after hours, mainly because the market is concerned that Micron’s capacity expansion plans may be more aggressive than expected.

☀ We remain confident in the DRAM upcycle, and our US$623 target price continues to be based on 4x the average FY2026/FY2027 book value per share.

☀ The DRAM upcycle is continuing, so there is no need for excessive concern going forward: on the one hand, management’s comments on the earnings call clearly supported our view that the DRAM price upcycle will extend through 1H27.

☀ Demand from both AI and traditional servers remains strong, while incremental supply in 1H27 will remain limited. Wafer output from new capacity will be concentrated in 2H27, and will only be sufficient to meet 50% to two-thirds of customer demand.

☀ On the other hand, although the market is concerned about capacity expansion beyond 2027, we remain optimistic on the long-term memory cycle.

☀ In 2026, we expect pure AI server memory — including HBM and LPDDR5X — to account for roughly 50% of total DRAM wafer demand, supported by the fact that the HBM-to-conventional-DRAM content ratio reaches 3.5x–3.8x.

☀ Entering 2027, as next-generation products transition to 1TB HBM4E and CPU-side LPDDR5X memory usage increases, AI memory demand is likely to double.

☀ In 2028, agentic AI could further boost memory demand by increasing requirements for CPU memory and workflow-related memory.

☀ In addition, newly signed long-term agreements should provide earnings support even during a downcycle.

☀ Pricing remained strong in fiscal 2Q26: according to market research data, average contract prices for conventional DRAM rose 90–95% in fiscal 1Q26, and we expect contract prices to increase by at least 50% in fiscal 2Q26.

☀ For example, Micron’s 64GB DDR5 server DRAM was priced at around US$800 in fiscal 1Q26, while current negotiations for fiscal 2Q26 indicate pricing of at least US$1,250, implying a sequential increase of more than 50%.

☀ Even after these price increases, Micron’s pricing still remains below that of SK Hynix and Samsung, leaving room for further hikes.

☀ Meanwhile, as the share of server products in the mix continues to rise, we also expect blended ASPs to trend higher.

$MU


the export of NVIDIA's H200 chipset will be a major variable shaking up the landscape of next year's Chinese AI accelerator market... The resumption of H200 exports can serve as a containment measure for NVIDIA to build an ecosystem defense line... NVIDIA to recover a meaningful market share in a short period

Summary: H200 re-entry into China gives NVIDIA opportunity to recover 10–25% lost market share while maintaining tech dominance via Blackwell/Rubin; bullish for NVDA.

[Research] Resumption of NVIDIA H200 Exports to China: Shifting Market Landscape and Strategic Implications

Hong Kong-based research firm Counterpoint Research, through its latest report, projected that the export of NVIDIA's H200 chipset will be a major variable shaking up the landscape of next year's Chinese AI accelerator market. The report analyzed that the H200's entry into the Chinese market offers NVIDIA an opportunity to recover 10–25% of its lost market share, while simultaneously establishing a new strategic balance point in the US-China technological hegemony competition.

1. US Confidence and NVIDIA's Pragmatic RecoveryThe permission for NVIDIA H200 exports to China is not merely a relaxation of regulations but is underpinned by the United States' sophisticated strategic judgment.

First is commercial recovery and containment. As CEO Jensen Huang mentioned, due to strong export controls, NVIDIA's market share in China plummeted from 95% to nearly 0%. The resumption of H200 exports can serve as a containment measure for NVIDIA to build an ecosystem defense line against rapidly emerging local Chinese chipmakers and to slow down China's 'AI chip self-sufficiency' through a sophisticated tiering strategy.

Second is the US's confidence regarding the technological gap. While the H200 remains a powerful chip, from the US perspective, it is already considered 'previous generation' technology. The next-generation flagship, Blackwell (B200), boasts three times the throughput of the H200, and Rubin, scheduled for 2026, will widen that gap even further. In other words, the US intends to maintain market control by releasing older models (H200) to China while maintaining technological dominance by monopolizing cutting-edge chips (B200, Rubin).

2. Persistent Performance Gap: H200 vs. Chinese Local ChipsThe issue is that even the H200, treated as 'outdated,' overwhelms competitors within China. Despite being two generations old, the H200 holds a clear performance advantage over Chinese-made chips.

Even Huawei's '910C', China's strongest contender, offers only about 76% of the computing performance and two-thirds of the memory bandwidth compared to the H200. The gap with other companies like Cambricon or Hygon is even larger. Even bearing the price premium, the proven H200 is an irresistible option for Chinese companies that prioritize 'Time to Market.' Depending on the scope permitted by Chinese authorities, this will be the driving force for NVIDIA to recover a meaningful market share in a short period.

3. 'Win-Win' Scenario: A Duet of Coexistence and CompetitionCounterpoint Research views a scenario where NVIDIA and the Chinese ecosystem coexist for the time being as the most likely outcome.

1) Dual AI Ecosystem: NVIDIA for Training, Local for InferenceChina does not need to win on every front. A pragmatic division of labor will likely form where the 'Inference' market—characterized by high cost sensitivity and massive volume—is dominated by competitive domestic chips, while the 'Training' sector—with high technological barriers—utilizes NVIDIA H200s. This is a realistic alternative to secure the competitiveness of domestic AI models while maintaining US dependency in cutting-edge areas at a manageable level.

2) H200: Not Dependence, but a 'Target to Overcome'The influx of H200s will not dampen China's will for self-reliance. Rather, the H200 acts as a clear and concrete 'performance benchmark' for Chinese chip manufacturers. The appearance of a tangible competitor, rather than an insurmountable wall, will serve as a catalyst providing stronger feedback loops and development motivation for Chinese companies.

3) ASIC-Centric Differentiation StrategyIf it is difficult to break through NVIDIA's GPU architecture and CUDA ecosystem head-on, China is likely to detour via ASICs (Application-Specific Integrated Circuits). As model architectures stabilize, ASICs, which are advantageous in terms of power efficiency and cost, demonstrate their strength. China's rapid manufacturing cycle provides a structural advantage for companies like Huawei and Cambricon to develop dedicated accelerators optimized for LLMs. However, restricted access to TSMC and yield issues at SMIC remain challenges to be solved.

4) Expansion into Full-Stack CompetitionUltimately, victory or defeat is determined not by a single chip but by the ecosystem. NVIDIA's strength comes from vertical integration combining hardware, CUDA, and developer tools. China also aims to build an independent full-stack ranging from computation to frameworks, models, and applications. This will take a long time, but once completed, it will be a decisive turning point to lower US dependency to the extent that individual chip performance gaps become meaningless.

$NVDA

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https://t.co/xxnmjcLKpw


I think Samsung Electro-Mechanics is still undervalued. I believe Samsung Electro-Mechanics will deliver explosive growth next year.

Summary: Bullish on Samsung Electro-Mechanics: undervalued MLCC/FC-BGA supplier to ASIC players, expects explosive growth next year.

Samsung Electro-Mechanics listed on KOSPI as 009150.KS

I think Samsung Electro-Mechanics is still undervalued.

A lot of people still see it as “just an MLCC company,” but in reality it supplies FC-BGA substrates to most ASIC players outside of NVIDIA.

I believe Samsung Electro-Mechanics will deliver explosive growth next year. https://t.co/g8FjxUWOSN


Summary: Nomura raises Samsung OP forecasts sharply on HBM4 share gains, pricing premium 30-40%, and commodity DRAM/NAND price beats; 2026/27F OP KRW243tn/322tn.

Nomura on Samsung Electronics: Raised 2026/27 operating profit (OP) forecasts to $168.2bn / $222.9bn

"We estimate that the rise in commodity memory prices significantly outpaced our expectation in 1Q26. We estimate that commodity DRAM/NAND prices rose by 90%/60% in 1Q26, which strongly beat our previous forecasts (DRAM +56%/NAND +40% q-q). We believe Samsung Electronics’ (SEC) HBM4 has better performance but higher production costs vs. competitors’, as the company fabricates core dies through 1C node and adopts base dies from 4nm-node foundry. While some of the customers require speed faster than 11.7Gbps, supplies from HBM suppliers are likely to be limited; thus, we see high potential for pricing premium of 30-40%. (Since low-speed HBM4 is also in tight supply, we do not think it can be viewed as a discount factor for memory companies.) Consequently, we anticipate SEC to benefit from high-speed HBM4 market and gain market share in HBM4. We raise our HBM ASP forecast for SEC, and raise 2026F HBM shipment growth from +112% y-y to +144% y-y. Reflecting these factors, we expect SEC’s 1Q26F memory OP to be at KRW44tn (+153% q-q), which is substantially above our previous forecast of KRW33tn. Although we anticipate commodity memory price growth to decelerate from 2Q26F, we think HBM ASP should rise q-q, based on rising mix of HBM4. We expect SEC to record 2026/27F OP of KRW243tn/322tn (+457%/+33% y-y; Fig. 6), and 2026/27F ROE of 42%/42%, which is higher than our previous estimates of 34%/35%."


Summary: Endorses bullish AMD report citing MI455X GPU demand potentially reaching 600K–800K units in 2026.

“We believe actual demand for MI455X GPUs could reach 600,000–800,000 units in 2026.”

wow https://t.co/krOaPloQbB

What an insanely bullish report.

$AMD https://t.co/8Xedh15823


Summary: Morgan Stanley relayed: robust 2nm demand pulling in TSMC EUV deliveries, implying bullish outlook for both TSMC capacity expansion and ASML scanner demand through 2027.

Morgan Stanley: Robust 2nm demand is driving a sharp increase in TSMC’s EUV tool deliveries

In a prior report, we mentioned that 2026 EUV scanner deliveries to TSMC have been pulled in given strong 3nm demand, as noted in AI Supply Chain: TSMC to expand 3nm capacity for major AI customer's growth? report. TSMC's 2025 EUV delivery has been pulled in 20%, and 2026 is increasing slightly vs. our prior checks. According to our recent checks, we also believe that it is possible that TSMC to have another 50% increase for its 2027 EUV scanners delivery given the huge demand for 2nm and pull-in schedule. We now expect TSMC N2 capacity to reach 100kwpm by 2026 and 140kwpm by 2027, with N3 capacity reaching 180kwpm in 2027.

$ASML


another 50% increase for its 2027 EUV scanners delivery given the huge demand for 2nm


Summary: Long INTC: thesis rests on US government commitment to making Intel succeed regardless of competitive position.

@0xgoodie @elonmusk My investment thesis for Intel is less about Intel’s own competitiveness and more about the U.S. government’s commitment to semiconductors.

From the U.S. perspective, Intel is a card that has to succeed. I believe the U.S. will do whatever it takes to make Intel successful.

@jukan05 @elonmusk Could you share your thought process on intel, appreciate it