Pegatron expects over 10-fold growth in AI server revenue this year amid strong demand for Nvidia GB-series, B200 and B300-platform servers, executives said, media report, adding Pegatron’s AI server business has expanded Neo Cloud clients to now including Hyperscalers as well, and full L11 & L12 models. Pegatron’s 2nd quarter net profit rose 1,463.8% year-on-year to NT$4.49 billion. $CRWV $NBIS #AIservers#

AI server giant Quanta Computer plans to raise up to US$2.2 billion (NT$71.2B) through a GDS (Global Depositary Share) offering in Luxembourg, media report, the biggest overseas fundraising by a Taiwan firm in nearly 20-years, media report, with the funds to be used for factory construction, component and materials purchases, more. $NVDA $AMZN $GOOGL $MSFT $META #Quanta# #AIservers#

AI Server Boom Triggers New MLCC Supply Squeeze AI server demand is driving a new MLCC shortage cycle, with tight supply expected through 2027 and potentially into 2028 if edge AI demand strengthens A single AI server rack can require 300,000–400,000 capacitors, far more than resistors or inductors, making MLCCs one of the biggest passive-component bottlenecks Murata and Samsung Electro-Mechanics remain the leaders in high-end AI server MLCCs, especially compact, ultra-high-capacitance parts. As they shift more capacity toward these premium products, consumer, automotive, and industrial orders are increasingly moving to Taiwanese suppliers Yageo has reached 90% MLCC utilization and is expanding capacity in Kaohsiung and Suzhou. Its combined MLCC, resistor, and tantalum capacity is expected to be 15% higher by the end of 2026 than a year earlier Walsin is running at 80–85% utilization and raised its 2026 capex budget from NT$500M–NT$1B to at least NT$3B. It expects 10–15% capacity growth in MLCCs and resistors, with a larger expansion planned for 2027 Pricing is also tightening. SEMCO is expected to raise prices by 25–30% for general-purpose X5R products and 10–20% for high-end X6S products used in AI servers The setup is increasingly favorable for Yageo and Walsin, as stronger AI demand, high utilization, rising backlogs, and order transfers from Japanese and Korean suppliers converge into the strongest MLCC expansion cycle in years

AI’s biggest bottleneck is moving data and that could still create huge opportunities for optical networking companies (Save this) The chart shows a 1.6T optical transceiver, a device that transfers data between AI servers, switches, GPUs, and fiber optic networks. 1.6T means it can theoretically move up to 1.6 terabits of data per second, or 1,600 gigabits and that is about twice the speed of an 800G connection. This technology is important because AI data centers contain thousands of GPUs that must constantly exchange information. As AI models become larger, slow connections can leave expensive processors waiting for data but faster optical links help reduce that bottleneck and allow AI clusters to operate more efficiently. This image shows two directions of travel. The TX path converts electrical data from a server or switch into light which travels through fiber. The RX path receives that light and converts it back into an electrical signal for another device. And inside the module are several key components. Optical DSPs process and correct the signal, laser drivers control the lasers, modulators place data onto the light, and photodiodes convert incoming light back into electricity. Amplifiers, timing chips, thermal sensors, power management devices, and high speed connectors help the system operate reliably. Optical fiber becomes more attractive at higher speeds because copper connections lose efficiency over longer distances. At 1.6T, copper may only be practical across very short distances while optical technology can move data farther with better bandwidth and lower signal loss. This creates an investment opportunity beyond the companies making AI chips. NVIDIA remains a major beneficiary because its AI systems require fast connections between GPUs. Broadcom and Marvell could benefit from their networking chips, custom silicon, and optical connectivity products. Coherent and Lumentum are important optical suppliers with exposure to lasers, photonics, and high speed transceivers while Applied Optoelectronics is a more direct transceiver play and has announced a volume order for 1.6T data center products. Arista Networks and Cisco could benefit by selling the switches and networking systems that connect AI servers. Chinese suppliers such as Innolight, Eoptolink and Accelink Technology could also benefit as China expands its AI data center infrastructure. If you enjoyed reading this, make sure to follow @MelvinInvests for more photonics, AI infrastructure and semiconductor insights and turn on post notifications so you don't miss a single update. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, check out the link below:

AI Hardware Demand Growth and Representative US-Listed Companies June 2026 Executive Summary Nvidia’s transition to the Vera Rubin (VR200) platform marks a significant escalation in AI infrastructure complexity and cost. Our BOM teardown of the next-generation Rubin rack reveals a ~2x increase in total rack cost to approximately $7.8 million (vs. ~$4 million for GB300), driven not solely by the GPU/CPU but by sharp revaluations across the supply chain. Key highlights from downstream components include: • PCB content value +233% YoY, the largest increase. • MLCC +182%, reflecting higher density and count (e.g., ~600k MLCCs per VR200 NVL72 server, +30%+ vs. GB300). • ABF substrates +82%, power solutions +32%, and liquid cooling +12%. These upgrades align with broader AI scaling: 800G/1.6T optical transceivers ramping aggressively, glass-based technologies advancing for packaging and interconnects, and hyperscalers prioritizing performance, power efficiency, and thermal management. We expect sustained multi-year tailwinds for the AI hardware ecosystem into 2027+, with Rubin-driven demand accelerating in H2 2026. Investment Thesis: While Nvidia (NVDA) remains the core beneficiary, the supply chain offers diversified exposure. We favor companies with direct exposure to high-growth areas like advanced PCBs, high-speed optics, and glass substrates/optical interconnects. Risks include execution on new capacity, potential margin pressure from rapid scaling, and geopolitical supply chain factors. 1. PCB: Sharpest Value Uplift in Rubin BOM Morgan Stanley’s detailed analysis shows PCB content in the Rubin rack surging +233% versus GB300. This reflects needs for higher layer counts, advanced materials, better signal integrity, and larger formats to support increased power and interconnect density in AI servers. US Representative: TTM Technologies (TTMI) – Leading US PCB manufacturer with strong positioning in high-complexity boards for data center/AI applications. TTM has invested in capacity expansions (e.g., new facilities) to capture AI-driven demand for advanced HDI and high-layer PCBs. 2. MLCC: Density-Driven Surge Nvidia’s VR200 NVL72 platform requires ~600,000 MLCCs per server, over 30% more than GB300. Combined with the +182% value increase in the BOM, this underscores tightening supply for high-capacitance, high-reliability MLCCs in power delivery and decoupling for AI accelerators. Exposure Note: The MLCC market is dominated by Asian players (e.g., Murata, Samsung Electro-Mechanics, Yageo). US-listed indirect exposure may come through broader electronics or power solution providers, but direct pure-play opportunities are limited. Watch for capacity utilization tightness benefiting the ecosystem. 3. Optical Communication: 800G/1.6T Ramp Accelerating Chinese leader Zhongji Innolight reported Q1 2026 net profit +262% YoY, driven by strong 800G/1.6T shipments, with expectations of significant full-year growth. This mirrors industry-wide momentum as AI clusters shift toward higher-speed optics for reduced latency and power in scale-out/scale-up networking. Nvidia’s investments in photonics and CPO further validate the trend. US Representatives: • Coherent (COHR) and Lumentum (LITE): Key players in optical components and transceivers; Nvidia has made substantial equity investments to secure capacity. • Corning (GLW): Major beneficiary via optical fiber, connectivity, and glass technologies (detailed below). 4. Micro-LED/Glass Substrates & Optical Interconnects: Strategic Partnerships Accelerating On May 20, 2026, BOE announced a cooperation MOU with Corning covering glass-based encapsulation carriers, foldable glass, perovskite substrates, and optical interconnect applications. This aligns with industry shifts toward glass cores for superior flatness, thermal stability, and integration in advanced packaging and photonics—critical for next-gen AI as organic substrates hit limits. US Representative: Corning (GLW) – Central to Nvidia’s optical strategy with multi-billion partnerships, new US optical factories, and expansion in fiber/photonics for AI data centers. Recent deals position GLW for 10x+ capacity growth in key areas. AI Hardware Demand Growth & US-Listed Representative Companies Table Component Demand Growth (vs. GB300) Key Drivers US-Listed Reps Investment Rationale PCB +233% value Higher layers, HDI, signal integrity TTM Technologies (TTMI) Direct AI server/backplane exposure; US capacity expansion MLCC +182% value; +30%+ count Power density in servers Limited direct (ecosystem via power suppliers) Supply tightness supports pricing/volume Optical Comm (800G/1.6T) Strong ramp (e.g., +262% profit ex.) Scale-out networking, CPO transition Coherent (COHR), Lumentum (LITE), Corning (GLW) Nvidia investments; transceiver/fiber boom Glass Substrates/Interconnects Emerging (MOU-driven) Packaging, photonics, thermal/optical Corning (GLW) Nvidia factory deals; US manufacturing tailwinds Power & Liquid Cooling +32% / +12% Higher TDP (e.g., 2300W GPUs) Indirect (ecosystem) Secondary but critical for rack deployment Source: Morgan Stanley BOM analysis, company reports, industry data. Growth metrics approximate from Rubin teardown. Outlook & Risks We project robust 2026-2027 growth in AI capex, with Rubin shipments catalyzing another leg-up in component demand. Optical and advanced substrate shifts could extend the cycle beyond traditional GPU focus. Hyperscalers’ vertical integration and US onshoring (e.g., Corning/Nvidia factories) add resilience. Key Risks: Cyclical capex pauses, yield/execution challenges on new tech (glass/CPO), commodity volatility in passives, and intense competition in Asia-heavy segments. Valuation multiples in the space have expanded; selectivity is key. Recommendation: Overweight select supply chain names with strong Nvidia alignment (e.g., TTMI for PCBs, COHR/LITE/GLW for optics/glass). Monitor Q2 2026 earnings for confirmation of Rubin ramp momentum.

The AI bull run is just getting started and here is how you want to position before the biggest spending wave (Save this). The chart shows hyperscaler capital spending rising from $491 billion in 2025 to an estimated $950 billion in 2026 and $1.4 trillion in 2027 and by 2030, spending could reach approximately $3 trillion. The right side of the chart is especially important because analysts have continued raising their estimates. The 2026 forecast increased from $731 billion to $950 billion, while the 2027 estimate rose from $833 billion to $1.4 trillion which suggests the AI infrastructure buildout is happening faster and at a larger scale than previously expected. Now here is how you can benefit from all of this. Nvidia is the obvious beneficiary but investors should also look at the companies supplying the less visible parts of the AI ecosystem. Credo Technology makes high speed connectivity products that allow AI chips, servers, and switches to communicate while Astera Labs provides connectivity solutions that link CPUs, GPUs, memory, and storage inside AI servers. As AI clusters become larger, these companies could benefit from the need to move data faster between processors. Celestica manufactures and integrates servers, networking systems, and other data center hardware for large technology customers while Fabrinet produces complex optical and electronic equipment for other companies. These businesses may benefit as hyperscalers outsource more of the manufacturing required to build AI infrastructure. Applied Optoelectronics is a more direct optical networking play and it has announced a volume order for 1.6T data-center transceivers, which are designed to move data between next-generation AI systems. Coherent and Lumentum also provide lasers, photonics, and optical components used in high-speed networks. Semtech supplies signal conditioning and connectivity technology that helps preserve data quality as transmission speeds increase while Arista Networks and Cisco could benefit from selling the switches and networking systems that connect AI servers across data centers. Marvell is exposed to custom AI chips, networking, and optical connectivity and ass cloud companies develop their own AI processors, Marvell could benefit from helping them design and connect those systems. The power side of the buildout could create another group of winners. Advanced Energy Industries supplies power conversion systems used in data centers and semiconductor equipment while Modine provides thermal management products, while Vertiv supplies cooling, power, and data center infrastructure. This is exactly why we’re positioned across the entire AI infrastructure stack at Milk Road, not just there big names. If you want to see the trades we’re making around this spending wave, join us using this link.

Smuggling AI servers into China didn’t change the downward trend in Super Micro’s gross margin Excerpt: "...servers sold for $510 million between late April 2025 and mid-May 2025..." Full article: At least ~10% of Super Micro’s 2Q CY2025 revenue was tied to servers reportedly smuggled into China. In theory, these should carry much higher margins, but gross margin still declined sharply to 9.6% (vs. 11.3% in 2Q CY2024). Two possibilities: 1. Margins in the legitimate business are simply too weak. This is consistent with my earlier view that AI server assembly margins are under pressure ( Super Micro’s structural disadvantages, including smaller scale and weaker execution, further amplify margin pressure. 2. Super Micro likely wasn’t the only one involved in smuggling. With alternatives available, buyer leverage increased, so margins on those sales were probably not as high as expected.

Sam Altman says AI usage could grow from 100,000 tokens per month in 2020 to hundreds of billions today, creating enormous demand for the companies powering the AI economy (Save this). That would represent roughly a million fold increase in six and a half years. Tokens are a way to measure how much information an AI model processes, so this growth shows that AI is moving from occasional chatbot questions to continuous use for coding, research, customer service, and business automation. OpenAI has reportedly seen its heaviest users consume around 100 billion tokens per month. Some extreme users have processed several hundred billion tokens in just 30 days. If usage continues expanding, demand will rise across the entire AI supply chain and here is some of the stocks that will benefit from this. NVIDIA remains the clearest beneficiary because its GPUs, networking products and software are used to train and run AI models. AMD could benefit as cloud companies and enterprises look for alternatives to NVIDIA’s chips. Broadcom and Marvell are important networking and custom chip suppliers and as AI models process more data, data centers need faster connections between servers, memory, and processors. Micron benefits from demand for high-bandwidth memory, which is essential for advanced AI processors. Super Micro Computer and Dell can benefit from demand for AI servers, storage systems, and complete data-center installations. Arista Networks supplies high-speed networking equipment for data centers, while Amphenol and TE Connectivity provide connectors and other components used to connect servers, racks, and communication systems. The AI boom also requires enormous amounts of electricity and cooling. Vertiv provides data-center cooling and power-management systems. Eaton and Schneider Electric benefit from electrical distribution, backup power, automation, and data center infrastructure. GE Vernova, Constellation Energy, and Vistra could benefit from the rising electricity demand created by AI data centers. The stronger AI adoption becomes, the more power companies and utilities may need to build or expand generation capacity. Equinix and Digital Realty provide data-center space and connectivity. Their facilities can benefit as companies lease additional capacity for AI workloads. The opportunity also extends to industrial automation. Siemens and Schneider Electric provide factory automation, industrial software, sensors, and control systems. If AI moves from the cloud into factories, warehouses, and robots, these companies could benefit from the physical deployment of intelligent machines. If you enjoyed reading this, make sure to follow @MilkRoadAI for more AI infrastructure and semiconductor insights and turn on post notifications on.

TAIWAN CHARGES 9 OVER AI SERVER SMUGGLING TO CHINA Taiwan prosecutors charged nine people, including former NVIDIA and Super Micro employees, over an alleged scheme to divert high-end AI servers to China. The group allegedly secured approval to buy 130 Super Micro servers equipped with advanced NVIDIA chips by claiming the hardware would remain in Taiwan. Prosecutors say 74 servers ultimately reached China through Hong Kong, Japan and Indonesia, while customs intercepted another 56 before shipment. The case comes as U.S. export controls continue restricting China’s access to advanced NVIDIA hardware. Source: WSJ