Equity research

IMHO - memory is at a place now where I believe you will see more bad news than good news. 

1. Margin sustainability
2. Impact on demand of consumer electronics
3. Chinese competition 

We know margins don't have much more room to grow, maybe peaking around 88-92%. 

Memory makers are selling near 90% gross margin on DRAM and it is becoming a question of how much longer can they keep up before the curve changes direction. 

Think of it like Nvidia. Nvidia faces the same questions - margins and competition.

Bernstein: Meta AI Compute 

> Bernstein estimates Meta currently has a massive ~20GW global footprint with another ~14GW coming online in the next few years.

> Concurrently, reports indicate Google has throttled Meta's compute capacity due to its own constraints, raising questions about whether Meta truly has "excess" capacity right now.

> Meta holds $35.2B in CoreWeave contracts (over one-third of CRWV’s backlog). Combined with Microsoft's ~$14B in contracts, nearly half of CoreWeave's backlog comes from companies that will likely be direct competitors by the time these contracts are up for renewal.

CoreWeave's Major Backlog Highlights

> Meta $META: $35.2B total (signed late 2025 and expanded April 2026 through Dec 2032, including early NVIDIA Vera Rubin deployments).

> Microsoft $MSFT: Estimated ~$14B across a series of relationships (effectively ~9 contracts).

> OpenAI: $22.4B total across multiple expansions signed in 2025.

> Jane Street: $6B multi-year infrastructure deal signed in April 2026, which included a $1B equity investment.

> NVIDIA $NVDA: A backstop agreement through 2032 worth up to $6.3B to purchase unsold CRWV capacity.

Rosenblatt note on Meta $META:

"Bloomberg reported today that Meta was planning a cloud business to sell AI computing power. The story noted that the company is considering selling access to various AI models hosted on its existing AI infrastructure, as well as "raw" computing capacity, as part of its Meta Compute initiative. During their annual meeting (May 27), Meta CEO Mark Zuckerberg told shareholders leasing cloud computing business was possible if they overbuild their data center capacity, which at the time he indicated they had not. Additionally, questioning on Meta's 1Q results call was focused on the ROIC of their CAPEX, so we are not surprised by today's messaging from Bloomberg's "people familiar with the matter." Our checks show no change in hyperscale posture toward GPU compute procurement, with GPU shortages the norm right now across the industry. We also do not think that Meta has the right to resell any of the capacity that they have leased from CoreWeave through 2032 to third parties."

No change in GPU compute procurement is bullish for $NVDA $AMD

Intel $INTC EMIB Supply Chain

Flip-Chip Assembly

Bumping

Powertech Technology $6239.TW   
Amkor Technology $AMKR 

Die Bond

ASMPT $0522.HK   
Kulicke & Soffa $KLIC 

Laser Marking

E&R Engineering $8027.TWO

Plasma Cleaning

E&R Engineering $8027.TWO

EMIB Substrate

IC Substrate

Ibiden $4062.T   
Unimicron $3037.TWO   
AT&S $ATS.VI   
Shinko 

ABF Film Lamination

Ajinomoto $2802.T   
Eternal Precision Mechanics $7795.TWO

Bridge Die Bond

Toray $3402.T

Electroplating

ASMPT NEXX

Laser via Drilling

Mitsubishi Electric $6503.T

Baking Oven 

Group Up $6664.TWO

Other Components

Silicon Capacitor 

AP Memory $6531.TW   
Samsung Electro-Mechanics $009150.KS

Silicon Capacitor Foundry 

Powerchip $6770.TW   
United Microelectronics $UMC   
Winbond $2344.TW

Information derived from Nomura Securities, but I included couple names in the supply chain I believe they missed - AT&S, Ajinomoto

Renesas Electronics: AI Infra & Compute

The Rise of AI Servers and Inference Compute

> Total general server shipments are expected to grow modestly (~1.3x) from 2024 to 2030. In contrast, AI Server volumes are projected to scale dramatically, growing ~2.4x, effectively doubling in volume over the period.

> While GPUs have driven the initial training wave, the market is seeing a major pivot toward AI ASICs and CPUs as the industry shifts heavily toward inference. AI ASIC shipments are projected to grow ~3.0x by 2030, vastly outstripping GPU growth (~1.5x) and standard CPU growth (~1.7x).

Revenue Drivers and Renesas’ TAM Expansion

> The primary structural tailwinds include the grid-to-rack transformation, an increase in system complexity requiring advanced MCU control, and a crucial architectural shift to 800V and vertical power architectures.

> Growth is driven heavily by Digital Power, followed by Memory Interfaces, and other supporting MCU components.

The Power Bottleneck: Grid-to-Core Architecture

> Grid Level: Managing power via Solid State Transformers (SST), Energy Storage Systems (ESS), and Uninterruptible Power Supplies (UPS) using Digital Controllers and high-voltage GaN (Gallium Nitride).

> Rack Level: Stepping down power using 800V DC/DC converters, hot-swap controllers, and Power Supply Units (PSUs).

> xPU Board Level: Delivering ultra-low voltage and high current directly to the processor via memory interfaces, high-speed optical module PMICs, and complex Vcore stages (utilizing integrated voltage regulators to optimize space and efficiency).

Exponential Power Demand Metrics

> Next-gen AI racks are projected to consume >1 megawatt (MW) of power, driving a >10x increase in power content per rack.

> The AI Infra & Compute Power TAM is scaling almost exponentially toward 2030, with Voltage Regulator Modules (VRM) making up the vast majority of this value, followed by Intermediate Bus Converters (IBC) and AC/DC stages.

> A single example leading next-gen AI board showcases the sheer complexity involved, requiring a GPU power solution with >10 Digital controllers and >100 Smart power stages, alongside a 48V IBC solution utilizing >30 MOSFETs. Dense packing drastically raises thermal constraints, making thermal performance and current density the ultimate competitive battlegrounds for silicon providers.

This is bullish for many power semi names. $STM $IFNNY $TXN $ON $WOLF

Tesla accelerates mass production, Chinese manufacturers race ahead collectively—is the 'EV moment' for humanoid robots arriving?

According to Nomura, Tesla is transitioning its Optimus Gen 3 from initial assembly into full-scale production, establishing the baseline for global manufacturing velocity. The company has adjusted its annualized capacity target for the Fremont facility (repurposed from the Model S/X production lines) upward to roughly 70,000 units. Furthermore, Tesla intends to build an additional 70,000 units of capacity in Austin by 2028, aiming for a long-term total capacity of 1.5 million units. Supply chain data indicates that 2026 Optimus shipments will reach approximately 25,000 units (+/- 10,000), with weekly production goals in September potentially rising to 1,000 units. UBS Group identifies the official rollout window for the Optimus Gen 3 between July and August, marking it as a pivotal market driver for the latter half of the year.

Outside of China, manufacturers continue to trail domestic production speeds. Figure AI and Boston Dynamics are both tracking annual shipments of roughly 500 to 1,000 units, while other international builders are averaging 100 to 200 units. Notably, Figure’s BotQ line has publicly achieved a manufacturing takt time of one unit per hour.

Nomura has upwardly revised its 2026 shipment forecast for Chinese humanoid robots to 40,000–50,000 units. This growth is propelled by intensified government procurement for embodied AI bases and a late-year consumer demand inflection point triggered by the introduction of cheaper models.

Market share is distributed across distinct tiers:

Tier 1 (Top 2 firms): Each shipped roughly 10,000 to 15,000 units (representing a 2x to 3x year-over-year increase).

Tier 2: Several mid-tier companies shipped approximately 3,000 units each.

Tier 3: Smaller players shipped between 500 and 1,000 units each.

Downstream application allocations for 2026 shipments are structured as follows:

Consumer Applications: 30%

Performance & Entertainment: 30%

Government Procurement (Data Collection): 20%

Education: 15%

Commercial & Industrial Applications: 3%–5%

UBS Group notes that state policy is actively shifting the Chinese robotics industry from experimental showcases to practical operational deployment across industrial, logistics, healthcare, and residential environments. The Ministry of Industry and Information Technology (MIIT) intends to deploy more than 10,000 units across over 100 scenarios by late 2027, while the Shanghai municipal government targets 100,000 factory-deployed units by 2030.

Investment Trends and Market Drag

Strategic capital activity in the global robotics sector intensified during the first half of 2026. Despite this momentum, overall market sentiment faces clear headwinds. UBS data reveals that China’s Humanoid Robotics Index has lagged behind the broader machinery index by roughly 9 percentage points year-to-date. This underperformance stems from delays in Optimus Gen 3 mass production, weak trading momentum, and capital shifting toward data centers and commercial aerospace.

Upcoming Catalysts

UBS highlights several milestones in the second half of the year capable of shifting market momentum:

> The official mid-summer launch of Optimus Gen 3 (July–August).

> Progress regarding Unitree’s IPO.

> The World Artificial Intelligence Conference in Shanghai (July).

> The World Robot Conference in Beijing (August).

Nomura highlights a major paradigm shift in how systems are trained: data acquisition is migrating toward "robot-free" methodologies, such as teleoperation, Universal Manipulation Interface (UMI), and first-person perspective (Ego) collection. These alternative techniques cost only 20% of physical robot-based methods and operate at a much faster pace.

As a hybrid training model consisting of 90% non-robotic data and 10% physical robot data becomes the industry norm, the demand for physical hardware dedicated to data collection will contract through 2026. Government entities remain the primary backers of these large-scale data collection hubs; establishing a single facility with a 1,000-unit capacity demands an investment of RMB 50–100 million and carries a 3-to-5-year payback period. While this reduces immediate hardware procurement pressure for OEMs, it elevates the demand for cross-platform standardization, data consistency, and high quality. Actual industrial integration still faces verification friction regarding cycle times, precision, and training costs.

Contract Manufacturing vs. In-House Supply Chains

Field research indicates a clear trend toward full-system contract manufacturing. Because hardware supply chains require heavy capital and feature long payback horizons, the majority of second- and third-tier OEMs are opting to outsource production. Over the long term, contract manufacturing's market share is expected to expand. Conversely, OEMs maintaining vertically integrated supply chains (such as Unitree Robotics) gain a direct pricing advantage by lowering their bill-of-materials costs.

Entrants coming from automotive supply chain backgrounds hold a long-term competitive edge due to their large-scale assembly experience and end-to-end management capabilities. Meanwhile, 3C electronics manufacturers currently hold a minor advantage in brand equity.

Nomura projects that mainstream, full-sized humanoid models will retail between RMB 150,000 and 300,000, while compact models will cost between RMB 10,000 and 100,000. Though prices have fallen by over 50% year-over-year in 2026, the rate of price deflation is projected to slow down in 2027.

Global Ecosystem Expansions

Strategic moves from major technology and automotive players are accelerating across the industry:

NVIDIA & OpenAI: NVIDIA $NVDA is actively funding an ecosystem that includes Chinese startup Unitree and South Korean enterprises like Hyundai. Concurrently, OpenAI renewed its focus on the sector in June by forming an "OpenAI Robotics" unit, initiating aggressive recruitment for AI, systems, and hardware engineering talent.

Corporate Financing & SPACs: Germany’s Neura Robotics secured a Series C funding round of up to USD 1.4 billion, aiming to manufacture 6,000 units this year and top 10,000 units annually by 2027. Agility Robotics announced its intention to go public via a merger with Churchill Capital Corp XI, establishing a pre-transaction valuation of USD 2.5 billion, with closure targeted for Q4 2026.

Automotive Integration: In June 2026, XPeng’s CEO assumed direct leadership of the company’s robotics branch, aiming to mass-produce its "IRON" humanoid robot by the end of the year. BYD also confirmed its own humanoid development program, planning an internal factory deployment of up to 20,000 units by year-end—marking one of the largest operational rollouts globally.

iM Securities: Is TSMC Bottlenecking Nvidia's Short-Term Growth?

> CoWoS Wafer Revisions: Due to slower-than-expected capacity expansion by TSMC $TSM and other CoWoS suppliers, iM Securities lowered its CY26 global CoWoS allocation forecast for AI accelerators from 1,380K to 1,096K wafers. Consequently, Nvidia's projected AI GPU production for the year has been cut from 11.14 million units (YoY +57%) to 9.24 million units (YoY +31%).

> Rubin Delays: Most notably, production projections for the next-generation Rubin GPU have been slashed in half, from 3 million units down to 1.5 million units.

> HBM Market Impact: Lower accelerator production drops total CY26 HBM demand from 4.89 billion GB to 4.23 billion GB. Memory manufacturers have adjusted their production down slightly to 4.33 billion GB, factoring in lower-than-expected demand and lower margins compared to conventional DRAM.

> Nvidia’s upcoming next-generation AI accelerator, the Rubin Ultra (slated for CY27), is facing technical hurdles that may force a significant specification downgrade. While still under negotiation between Nvidia and memory vendors, a 384GB scaled-down version is being considered instead of the original 1TB (1,024GB) target.

> CoWoS-L Size Limitations: The physical limit of the CoWoS-L interposer is 8,150 mm^2. The original 4-die plan requires 6,750mm^2. While mathematically possible, scaling the interposer up dramatically increases substrate warpage, concentrates stress on the corners, degrades bump fatigue life, and tanks packaging yields. Returning to a 2-die architecture automatically drops the maximum HBM cubes from 16 to 8.

> TSMC's CoPoS Alternative is Too Late: TSMC’s next-gen solution to bypass this size limit is CoPoS (Chip on Panel on Substrate). However, because TSMC is only just beginning to select equipment and component vendors, mass production is not expected until the second half of 2028 (2H28). This leaves a packaging bottleneck that will stress Nvidia's growth through next year.

> HBM4E Stacking Yields: Memory manufacturers are also struggling with the production yields of stacking 16-layer HBM4E, though this is flagged as a memory vendor issue rather than a TSMC-inflicted constraint.

> Total AI Accelerator chip volume for CY26 is projected to hit 17,865K (17.87M) units, reflecting a 51% YoY growth.

> Nvidia $NVDA commands 56% of the total CoWoS capacity allocation (640K wafers), yielding 9,242K chips (+31% YoY).

> Broadcom $AVGO captures 272K CoWoS wafers (a massive 206% increase), resulting in 5,354K chips (+70% YoY). This is heavily anchored by Google's TPU, which takes 75% of Broadcom's share (4,027K chips). Meta's MTIA accounts for 1,017K chips.

> AMD $AMD AI GPUs take up 70K wafers, translating to 875K chips (+22% YoY), split between MI350X (560K) and MI400X (315K).

> Global HBM demand for CY26 is expected to reach 4,234 million GB (4.23B GB), marking an explosive 95% YoY growth.

> Nvidia alone consumes 2,427M GB (roughly 57% of total market demand).

> Broadcom represents 1,162M GB of HBM demand (+115% YoY), dominated heavily by the TPU at 851M GB.

> AMD accounts for 282M GB of demand, with the upcoming MI400X utilizing ultra-dense HBM4 configurations (384GB per accelerator using 48GB cubes).