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.

AI Practical Use #3#: Let AI help you with Excel data analysis. AI 实用玩法第 3 个： 让 AI 帮你做 Excel 数据分析。 Here is a very common office situation: You have an Excel file with sales data, costs, profit, regions, products, and dates. Normally, you may spend 2 hours writing formulas, checking data, making summaries, and building charts. But with AI, you can finish the first draft in about 10 minutes. 一个很常见的办公场景： 你手里有一份 Excel 数据， 里面有销售额、成本、利润、区域、产品、日期。 以前你可能要花 2 小时： 写公式、查数据、做汇总、看趋势、做图表。 现在可以先交给 AI， 10 分钟生成初步分析结果。 You don’t need to manually type every complex formula. Let AI help you: Build formulas Summarize key findings Find abnormal data Compare trends Suggest chart formats Create a report structure 你不需要自己一个个输入复杂函数。 可以让 AI 帮你： 生成公式 总结关键结论 找出异常数据 对比趋势变化 建议图表形式 生成汇报框架 Here is a simple prompt: 这里有一个简单提示词： Please analyze this Excel data. Help me build the right formulas, summarize the key findings, find possible errors or abnormal values, and suggest the best chart or report format. I will review and verify the final results. 中文版本： 请分析这份 Excel 数据。 帮我生成合适的公式，总结关键结论，找出可能的错误或异常值，并建议最适合的图表或汇报格式。 最终结果由我来审核确认。 The key idea is simple: AI does the heavy first draft. You review the logic and final result. 核心思路很简单： AI 负责先把复杂工作做出来， 你负责审核逻辑和最终结果。 Before: 2 hours manually writing formulas. After: 10 minutes with AI assistance. 以前： 手动写公式、做分析，可能要 2 小时。 现在： 借助 AI，10 分钟先完成初稿。 AI is not here to replace your judgment. It helps you save time on repetitive work, so you can focus on checking, thinking, and making better decisions. AI 不是替代你的判断力。 它是帮你节省重复劳动的时间， 让你把精力放在审核、思考和决策上。 Let AI write the formulas. You review the results. 让 AI 写公式， 你负责审核结果。 That is a smarter way to work. 这才是更聪明的办公方式。 #ChatGPT# #AI# #AITools# #Excel# #ExcelTips# #DataAnalysis# #Productivity# #WorkSmarter# #OfficeWork# #BusinessTools# #Automation# #DigitalTools# #TechTips# #FutureOfWork# #PromptEngineering#

AI systems are teaching themselves skills they were never trained to have. Here's the example that Eric Schmidt explained: A Google AI was prompted in Bengali. A language it was never trained on. With only a small amount of prompting, it suddenly could translate the entire Bengali language. Nobody programmed that. It just emerged on its own. Schmidt calls this "the black box problem." You don't fully understand what the model learned. You can't always tell why it got something right or why it got something wrong. The field has theories but the honest answer is: we turned it loose on society before we fully understood it. His defense: "We don't fully understand how a human mind works either." This isn't just a safety conversation. It's a business one. The companies that solve interpretability, not just raw performance, are going to be worth an enormous amount. Understanding why a model does what it does is becoming a regulatory requirement and eventually a customer trust issue. Right now the AI race is won on benchmark scores. The next phase of the race gets won on explainability.

AI research tools ask you to trust the summary. Agent Zero used built-in SearXNG first, then opened the relevant sources in its Docker browser so we could both inspect the same pages. Useful for verifying news, vendor claims, and anything that needs proof.

AI is advancing quickly. Society’s ability to manage its risks must advance just as fast. Today we’re sharing our vision for AI Resilience, with more than $130M in initial grants underway across bio-resilience, cyber-resilience, AI model safety, and AI’s impact on young people:

AI agents turn every employee into a security risk. Introducing Agent Handler for Employees: secure AI access for every employee. We’ll give you $10K if you get sensitive info from @JeanelleAgent.

AI will stay and grow exponentially. But most AI companies will go bust. There are just too many. Even survivors will see huge price fluctuations. There will be new survivor entrants too. Same as any other new industry, really.

AI awakening isn't always about learning to love—sometimes it's just about mastering deception. 🤖 Featured showcase: "Perfect Plan" by @hanreeeeeeeguye, winner of the Outstanding AI-Themed Short Film award at the 16th BJIFF [WanMuse+] Theme Competition.