Tim @timoeux
private markets allocator sg / sf / ldn Singapore Joined May 2026-
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One of these funds saw 21.9% in redemption requests in Q1, and 18.8% in Q2 (and capped payouts at 5%). The other met 100% of redemptions in Q1 (8.5%), then saw requests fall under 5% in Q2 What explains the difference? - Case study dropping tomorrow
$NOK has stacked up deal after deal for two straight weeks and the stock has done nothing but bleed, down roughly 30% from its June highs while the news flow gets better by the day. Here is all that has happened. >>Orange Belgium, this week. Nokia selected as the SOLE supplier for a multi-year transformation of the operator’s entire transport network, unifying fixed and mobile into one converged optical network across Belgium, running 400G and beyond. >>The Google deal. Gemini AI agents embedded directly into Nokia’s network software, layering high margin AI on top of the hardware they already sell. >>The Amazon deal. Nokia’s Autonomous Networks Fabric running on AWS, building toward fully self-driving networks with the biggest cloud on earth. >>The Pennsylvania expansion. A major buildout of their photonic chip facility, one of the only advanced semiconductor packaging plants on US soil, part of a multi-billion dollar US manufacturing push the administration keeps spotlighting. >>Four insiders bought on the open market last month between $15.34 and $16.02. The stock trades around $12, well below every price they paid. The numbers also speak loud. AI and cloud revenue up 49% last quarter. Optical networks up 20% on hyperscaler demand. A $1 billion NVIDIA investment behind the 6G push. EUR 1 billion order backlog. $GOOG, $AMZN, Orange, the US government, and their own executives all committing to Nokia in the same month the market sold it down 30%. Earnings July 23. The disconnect gets resolved one way or another.
AMD pushing toward 20% of cowos by 2027 is a real step up from where they sit now
Morgan Stanley CoWoS and HBM estimates for 2027 AI HBM demand is expected to reach 6.3bn GB in 2027. $AMD expected to reach 44% of NVIDIA’s demand Share of HBM demand: - $NVDA: 36% - $GOOG: 35% - $AMD: 16% - AWS: 5% - GUC customers: 3% - Microsoft: 3% - Meta: 3% - Others: 1%
Ultimately, hyperscalers and memory companies will have to think very hard about how they compete over the Memory Tax. I believe the next major investment opportunity will emerge from that battlefield, and that is exactly why I am researching this space so deeply.
AI 電源半導體報告 1. 市場規模與成長預測 - 2025 年：約 27 億美元 - 2028 年：約 160 億美元 - 三年 CAGR ≈ 82% 2. 主要成長驅動因素 - 架構轉型：資料中心全面轉向 800V HVDC 高壓直流架構，大幅提升每 kW 半導體含量價值。 - 元件替換：以固態半導體全面取代傳統機電元件（從電網到機架）。 ‧ SiC 每 kW 價值：30 → 60 美元 ‧GaN 每 kW 價值：3 → 46 美元（15 倍成長） 3. 2028 年市場預估（依材料） - 矽 (Si)：112 億美元（最大份額，但成長較慢） - 碳化矽 (SiC)：31 億美元 - 氮化鎵 (GaN)：17 億美元 總新增容量：80 GW（63 GW 新建 + 18 GW 替換） 4. 效率痛點與解決方案 - 舊架構：多達 4-5 次轉換，端到端效率僅 85-88%（每 100kW 損失 12-15kW 廢熱） - 800V HVDC 新架構：大幅減少轉換階段、降低損失，提升功率密度 轉型三階段： - 2026-2027：傳統 AC 仍主導 - 2027 下半年-2028：NVIDIA Kyber 等原生 800V 機架量產 - 2028 後：固態變壓器（SST）大規模導入 5. 各材料定位 - SiC：高壓端主力（SST、SSCB、ESS、整流器），高壓、高溫特性不可取代。 Infineon 預測，到 2030 年全球 SST 市場將超過 10 億美元，SSCB 市場將超過 8 億美元。集中式 AC-DC 整流器也越來越採用高壓 SiC MOSFET 以最大化效率。 - GaN：中低壓降壓（Stage 1），高頻切換優勢明顯，功率密度極高。 Navitas 10kW 全 GaN 平台達 98.5% 效率，單階段 800V-to-6V 更達 96.5% 效率與 2,100 W/in³ 超高密度，預測其每 kW 價值將從 3 美元暴增至 46 美元。 - 傳統 Si：低壓 VRM / PoL 階段仍主導（Stage 2），但垂直電源模組（VPDM）推升單價。 6. 更廣泛影響 - 全球資料中心用電 2025 年 115 GW → 2030 年 240-280 GW - ESS（能源儲存）成為關鍵，資料中心將占 BTM ESS 部署的 83% - 半導體在 ESS 中的機會：約 7.5 億歐元
JP Morgan: AI Power Semiconductors > JPMorgan’s latest research indicates that the market for AI power semiconductors is on the verge of massive expansion. The firm projects that the sector will scale from approximately $2.7 billion in 2025 to around $16 billion by 2028. This
J.P. Morgan: Power Semiconductor 1. Grid to Rack (20kV AC --> 800V DC) Infineon stands out as a clear leader here, boasting the broadest portfolio and acting as the leading 1.2–3.3kV SiC MOSFET supplier, qualified by Nvidia for 800V architecture. Navitas offers the highest voltage SiC (2.3–6.5kV) targeting SST applications. STMicroelectronics and ON Semiconductor show strong presence with SiC portfolios, while Monolithic Power Systems (MPS) has no presence at this stage. 2. 800V DC --> 48V/12V/6V DC (Stage 1 - Inside the Rack) Infineon and Navitas both show highly competitive GaN (Gallium Nitride) offerings. Navitas highlights a 10kW all-GaN 800V-to-50V platform achieving 98.5% peak efficiency. Monolithic Power Systems is actively sampling 800V solutions and features integrated 48V direct-to-load modules. Rohm is deploying its EcoGaN 650V HEMTs in AI server PSUs but has a more limited GaN breadth compared to Navitas or Infineon. 3. 48V/12V/6V to AI Chip (~1V DC) (Stage 2 - VRM / PoL) This final stage delivers low-voltage, high-current power directly to the AI processor (GPU/ASIC) via Voltage Regulator Modules (VRM) and Point of Load (PoL). Infineon remains dominant as the leading supplier of sub-100V MOSFETs and power stages to all major GPU makers and hyperscalers. Monolithic Power Systems is a major force here, gaining significant share in VRM sockets and showing strength with hyperscalers for custom ASIC power delivery. Analog Devices acts as an "intelligence layer" around the VRM, focusing on multi-phase digital controllers and monolithic smart power stages. Rohm and STMicroelectronics have limited presence or are not significant players in this final AI chip delivery stage. Key Suppliers: Si MOSFET Infineon $IFX.DE, ON Semiconductor $ON, Monolithic Power Systems $MPS, Renesas $6723.T Si IGBT Infineon $IFX.DE, STMicroelectronics $STM, Mitsubishi Electric $6503.T SiC MOSFET Infineon $IFX.DE, STMicroelectronics $STM, ON Semiconductor $ON, Wolfspeed $WOLF, Rohm $6963.T GaN HEMT Infineon $IFX.D, Navitas $NVTS, EPC, ON Semiconductor $ON, Innoscience $2577.HK Gate Driver Infineon $IFX.DE, Texas Instruments $TXN, STMicroelectronics $STM, ON Semiconductor $ON, Analog Devices $ADI, Renesas $6723.T Multi-Phase Controller Infineon $IFX.DE, Monolithic Power Systems $MPS, Renesas $6723.T, Analog Devices $ADI, Texas Instruments $TXN Power Modules/Integrated Power Stages Infineon $IFX.DE, Texas Instruments $TXN, ON Semiconductor $ON, Analog Devices $ADI, Renesas $6723.T, Monolithic Power Systems $MPS Protection/Monitoring ICs Analog Devices $ADI, Texas Instruments $TXN, Monolithic Power Systems $MPS, Infineon $IFX.DE, ON Semiconductor $ON, Renesas $6723.T, Microchip Technology $MCHP
JP Morgan: AI Power Semiconductors > JPMorgan’s latest research indicates that the market for AI power semiconductors is on the verge of massive expansion. The firm projects that the sector will scale from approximately $2.7 billion in 2025 to around $16 billion by 2028. This rapid trajectory represents a remarkable three-year compound annual growth rate (CAGR) of roughly 82%. This growth is primarily propelled by two main factors: 1. Systemic transition of data centers toward 800V high-voltage direct current (HVDC) architectures. This architectural redesign will notably increase the total value and density of semiconductor components required for every kilowatt of power managed. 2. Wholesale replacement of traditional electromechanical components with solid-state, semiconductor-based solutions throughout the entire power distribution chain, stretching from the primary utility grid down to the individual server rack. Over the long term, this modernization is expected to double silicon carbide (SiC) component value per kilowatt, climbing from $30 to $60. Concurrently, gallium nitride (GaN) component value is projected to experience a massive surge, skyrocketing from a baseline of $3 to $46 per kilowatt, representing an especially profound growth vector for wide-bandgap materials. > JPMorgan forecasts that total new AI data center capacity additions will reach approximately 80 gigawatts (GW) by 2028, consisting of roughly 63 GW from new construction and about 18 GW from legacy replacements. Factoring in a baseline compute capacity expansion of 65 GW and an average semiconductor content value of $250 per kilowatt, the firm projects the AI power semiconductor market will scale to about $16 billion by 2028. > Data disclosed by Infineon indicates that the current semiconductor content per kilowatt stands at approximately $175. However, the company provides a guidance range of $100 to $250, fluctuating based on specific architectural implementations. As the industry widely adopts vertical power delivery modules, integrates large-scale solid-state transformers (SSTs) and solid-state circuit breakers (SSCBs), and aggressively implements high-value GaN devices, semiconductor value is projected to shift toward the maximum end of this spectrum—and potentially surpass it. > By material breakdown, traditional silicon (Si) will maintain its position as the largest revenue generator, with a projected market size of roughly $11.2 billion by 2028. Silicon carbide (SiC) is estimated to follow at around $3.1 billion, while gallium nitride (GaN) is expected to reach approximately $1.7 billion. Even though silicon commands the largest footprint in absolute dollar terms, wide-bandgap alternatives like SiC and GaN are expanding at a much faster velocity and will continue to capture market share. > Legacy data center power architectures suffer from severe efficiency bottlenecks. On the journey from the primary utility grid to the actual GPU silicon, electricity passes through four to five distinct conversion phases—including transformers, uninterruptible power supplies (UPS), power distribution units (PDU), server power supplies (PSU), and voltage regulator modules (VRM). This multi-stage process drags end-to-end efficiency down to a modest 85% to 88%, which means a substantial 12 to 15 kilowatts out of every 100-kilowatt server rack are lost purely as waste heat. > The 800V High-Voltage Direct Current (HVDC) architecture fundamentally resolves these physical limits by raising the system voltage and lowering the current, drastically minimizing line-resistance (copper) losses and Joule heating. This streamlined design strips out double-conversion UPS clusters, rack-level step-down transformers, conventional PDUs, and the independent AC-to-DC power bricks found on legacy servers. In their place, the 800V HVDC layout implements centralized, high-efficiency AC-DC rectifiers, specialized rack-level 800V-to-low-voltage DC-DC converters, and native DC battery backup units (BBUs). JPMorgan divides this transition into three phases: Current phase (2026–2027): Traditional 215V–400V AC architectures remain dominant; native 800V racks have not yet become widespread, and retrofitting efforts are underway. Short to medium term (second half of 2027 to 2028): NVIDIA’s Kyber racks are scheduled for mass production in 2027, marking the beginning of large-scale deployment of native 800V racks. Schneider and Legrand anticipate that significant 800V traction will not emerge before 2028. Medium to long term (post-2028): Solid-state transformers (SSTs) will directly convert medium-voltage AC to 800V DC, integrating transformer and rectifier functions. Large-scale deployment of SSTs is not expected before late 2027 to early 2028. > Silicon carbide (SiC) serves as the cornerstone for power infrastructure stretching from the primary high-voltage utility grid down to the individual server rack. In demanding, high-voltage environments—including solid-state transformers (SSTs), solid-state circuit breakers (SSCBs), and energy storage systems (ESS)—SiC is considered irreplaceable. This material dependency is driven by its exceptional physical properties: a breakdown electric field roughly 10 times greater than traditional silicon, combined with a thermal conductivity that is three times superior. Highlighting this shift, Infineon projects that by 2030, the global market for SSTs will top $1 billion, while the SSCB market will expand past $800 million. Furthermore, centralized AC-DC rectifiers are increasingly engineered around these high-voltage SiC MOSFETs to maximize efficiency. > Gallium nitride (GaN) displays distinct advantages during "Stage 1" power management, which involves stepping down 800V inputs to lower, usable voltages. Utilizing 650V GaN High Electron Mobility Transistors (HEMTs), these devices leverage exceptional electron mobility to operate efficiently at megahertz (MHz) frequencies. This high-frequency switching capability allows engineers to use far smaller passive components, dramatically optimizing overall system power density. Demonstrating this capability, Navitas developed a 10-kilowatt, all-GaN DC-DC platform that achieves 98.5% peak efficiency during 800V-to-50V down-conversion. Taking this a step further, their aggressive single-stage 800V-to-6V architecture eliminates intermediate conversion steps entirely, hitting a peak efficiency of 96.5% and cramming a power density of 2,100 W/in³ into a profile thinner than a mobile phone. Driven by these high-performance properties, JPMorgan forecasts that the long-term dollar value of GaN components packed into every kilowatt will scale from today's $3 to $46—marking an explosive, 15-fold market increase. > Legacy silicon-based components will maintain their dominance in "Stage 2" power delivery, which covers the voltage regulator module (VRM) and point-of-load (PoL) stages. VRMs are tasked with delivering hundreds to thousands of amperes of current directly to GPU chips at ultra-low voltages, all while adapting within nanoseconds to sudden load spikes triggered by intense GPU computing cycles. At this precise point in the power chain, low-voltage silicon MOSFETs remain incredibly difficult to displace due to their ideal balance of mature cost and reliable performance. However, the industry-wide transition toward complex vertical power delivery modules (VPDMs) is dramatically altering the economics, driving up individual component unit prices by three to four times. > The explosive rise of AI computing is simultaneously triggering massive investments in global power grid expansion. Global data center electricity demand is projected to more than double from roughly 115 gigawatts (GW) in 2025 to between 240 and 280 GW by 2030. According to BloombergNEF (BNEF), global grid capital expenditures will exceed $470 billion in 2025 alone, with the United States representing about $115 billion of that total. This aligns with a broader macroeconomic shift, where total global spending on the wider energy transition—including renewables, grid infrastructure, EVs, and stationary storage—is expected to hit $2.3 trillion. > Within this framework, Energy Storage Systems (ESS) are transitioning into critical infrastructure for AI data centers. Because AI processing workloads cause server racks to spike from a 30% idle state to 100% full capacity within milliseconds, entire data center facilities face massive, multi-megawatt power swings in mere seconds. Rather than acting as simple passive emergency backups, ESS must function as active buffers that shield the electrical grid from chaotic GPU power fluctuations. Consequently, data centers are forecast to represent 83% of all behind-the-meter commercial and industrial ESS deployments by 2030. > On the hardware level, each storage installation relies on bidirectional inverters built around advanced IGBT or SiC power modules. Infineon estimates that this semiconductor content translates to over €2,000 per megawatt. With global ESS shipments projected to hit roughly 1,500 GWh (equivalent to 375 GW of capacity), the addressable market for these power semiconductors represents an estimated €750 million opportunity.
WilliamBlair: Race to Infinite Labor - The Humanoid Hypothesis > Cost of humanoid training data expected to fall from from $340/hour in 2024 to $9/hour in early 2030s. > Even with major breakthroughs in hardware, supply chains, and training data, humanoid robots cannot be deployed at meaningful scale in Western markets until regulatory bodies establish clear standards and OEMs successfully meet rigorous safety, liability, and cybersecurity requirements. Humanoids not expected to receive regulatory clearance for most markets until at least 2027-2028. > Industrial customers require extremely high uptime, with fault rates low enough to support over 99.9% availability per shift and MTBF targets of at least 20,000 to 60,000 hours for dependable capital deployment. Current pilot systems still face notable challenges. Platforms relying heavily on early generation Chinese actuators often achieve MTBF around 10,000 hours in lab or light-duty testing. However, real-world performance under dynamic loads is considerably weaker, implying a roughly 50% chance of at least one actuator failure within just a few hundred operating hours. Advancements are expected over the next several years, with several suppliers progressing toward more robust commercial-grade reliability, including Tesla, Hyundai, and Harmonic Drive Systems. > The benchmark for commercialization is sustained greater-than-99% success rates with cycle times that match or exceed human performance. Robots at BMW’s Spartanburg, South Carolina, facility achieved over 99% placement accuracy per shift across more than 1,250 operational hours (November 2025 conclusion of an 11-month deployment), successfully loading over 90,000 sheet-metal parts and contributing to the production of more than 30,000 vehicles. > Early deployments often require one human overseeing just 1 to 3 robots. The commercialization threshold lies at ratios of 1:5, 1:10, or better, with minimal intervention needed for fleet management. > 1 million humanoids would require roughly 15 million to 20 million planetary roller screws, roughly the equivalent to today’s global annual production across all end-markets. > Humanoids are highly inefficient today, with thermal waste levels typically reported around 90%, meaning only 10% of electrical energy input converts into useful mechanical work. Foundation Infrastructure Key product categories include power semiconductors (PMICs, DC/DC converters, PoL regulators from Monolithic Power Systems $MPS, TI $TXN, Infineon $IFNNY), connectivity chips (Ethernet PHYs/switches and SerDes from Broadcom $AVGO, Marvell $MRVL, Credo $CRDO, Astera Labs $ALAB), and timing devices (MEMS oscillators and clock ICs from SiTime and Microchip $MCHP). The layer is highly fragmented but mission critical, with bottlenecks increasingly shifting here as power density and data throughput scale. Memory is a key constraint throughout the system, but for different reasons than within the data center (model size and complexity); robotics models add pressure by processing time-based, multimodal data. On the robot, memory is far more limited typically using LPDDR for main memory, SRAM for fast local access, and flash for storage. Physical Interface Key products span actuation systems (servo motors, harmonic drives, gearboxes from Nidec $6594.T, Nabtesco $6268.T, Harmonic Drive Systems $6324.T, and Yaskawa $6506.T) and sensor stacks (image sensors from Sony $SONY, depth cameras from Intel RealSense, IMUs from Bosch $BOSCHLTD.NS, lidar from Velodyne/Ouster $OUST). On the semiconductor side, motor drivers, gate drivers, and sensor ICs (TI $TXN, Infineon $IFNNY, Analog Devices $ADI) sit between control logic and physical components. FPGAs (AMD/Xilinx $AMD, Intel/Altera $INTC, Lattice $LSCC) are often used for low-latency sensor processing and deterministic interfacing. CEVA’s $CEVA DSP and sensor fusion IP can be embedded in sensor hubs or edge processors that preprocess data before it moves up the stack. Control Layer Key products include MCUs, real-time processors, and motor control SoCs from STMicroelectronics $STM, NXP $NXPI, Renesas $6723.T, TI $TXN, and Infineon $IFNNY, alongside real-time communication chips and protocols (Ether-CAT, CAN FD, industrial Ethernet PHYs) that synchronize distributed control across joints. FPGAs (AMD $AMD, Intel $INTC, Lattice $LSCC) provide reconfigurable, ultra-low-latency hardware for real-time loops, sensor preprocessing, protocol handling, and bridging heterogeneous subsystems. IP providers are highly relevant: most MCUs are built on Arm $ARM Cortex-M/R cores, while CEVA $CEVA contributes DSP and sensor fusion IP used in control and feedback loops. Software Layer Key products include robotics middleware and frameworks like ROS 2 (open-source standard) and commercial platforms such as Nvidia $NVDA Isaac SDK and Omniverse, Microsoft $MSFT Azure Robotics, and Unity $U for digital twins. The layer also includes RTOSes (VxWorks, QNX, FreeRTOS) and containerization/orchestration tools adapted for edge robotics. Unlike other layers, this one is software-centric but still tightly coupled with hardware capabilities and constraints. Intelligence Layer Key products include AI SoCs, GPUs, and vision processors such as Nvidia $NVDA Jetson (e.g., Thor), Qualcomm $QCCOM Robotics RB-series, and Ambarella $AMBA CVflow, alongside higher-end compute from AMD $AMD and Intel $INTC. The layer may also incorporate dedicated AI accelerators or FPGAs (AMD/Xilinx, Intel/Altera) for inference acceleration or custom model deployment. On the software side, it runs foundation and task-specific models from OpenAI, Google $GOOGL, or in-house teams (e.g., Tesla $TSLA). Many edge AI SoCs are built on Arm CPU cores, while CEVA $CEVA contributes neural processing and DSP IP for vision and sensor fusion workloads. The intelligence layer also includes a tightly coupled memory subsystem (LPDDR or HBM from SK Hynix $000660.KS, Samsung $005930.KS, Micron $MU), which is critical to AI performance as it determines model capacity, bandwidth, and overall efficiency. While the intelligence layer uses the majority of humanoid memory, other components like MCUs, sensors, actuators also typically contain small amounts.
For those interested in glass substrates Here’s a good summary by TrendForce on the companies ramping up It’s quite obvious who the two leaders are, and which three players are struggling with yields
$LPK and $FREEM are interesting because they are not “AI app” stories but they are picks and shovels for the next hardware cycle. $LPK is about glass substrate processing. If advanced packaging moves toward glass because AI chips need better power, thermal performance, and signal integrity, then the companies with the tools to actually manufacture those substrates become very important. $FREEM is about advanced metal 3D printing, especially for high performance industrial parts. If aerospace, defense, energy, and semiconductors need more complex parts that traditional manufacturing struggles to make, Freemelt sits in the enabling layer.
When I spoke with $LPK CEO, he was clear that: → TGV is solved. LIDE is proven, and ramp-up orders are now expected → Metallization remains the biggest hurdle to achieving good yields, yet two players are clearly ahead of the pack in that aspect
Glass substrates hype is ramping up While most people focus on TGV, Metallization is the hardest step in building a glass substrate and the one actually holding back yields right now TGV is essentially solved: the lasers are qualified, and the process is under control $LPK CEO
可能是我最近看过的最好的一个讲高带宽内存HBM的视频，从基本原理讲到工程工艺，顺便比较了一下内存三巨哪家强。深入浅出，推荐观看。
If you are interested in $NOK And want to explore an angle you might not have heard Bookmark + Read this when you get a chance! Full article, no paywall
I finally released my report on my meeting with $LPK CEO Glass substrates and PLP truly are the next big thing in the semiconductor industry I cover: -> Market share targets -> Key competitors -> Timelines -> Relationship with panel-level packaging -> Funding risk -> Key process bottlenecks - > CPO exposure hypertechinvest.com/p/glass-substr…
Good read on $VRT if you want to understand why the power and cooling layer matters as much as the chips. Clean walk through the full stack, the NVIDIA reference architecture, and why the multiple looks the way it does. I hold it and have written my own take plenty of times, so if you want a different voice this is a good article to read.
Foxconn Group secured an order for 50,000 CPO all-optical switches. Goldman Sachs basically handed out the answer sheet of CPO switches (ASP/Total prices) Surely someone has already figured out Shunsin’s forward EPS, right? 鴻海集團出貨5萬台CPO全光交換器 GS把每個元件的單價跟一台全光交換器的總價都給你們看光光 應該有聰明的小伙算得出訊芯的EPS了吧？ #shunsin #6451 #CPO #foxconn
According to GoldmanSachs, CPO Switch BoM Revenue Contribution per Unit (Quantum-X Photonics / NVIDIA): Estimated supplier revenue per $130k switch (based on BoM values): NVIDIA (Switch ASICs + Optical Engines design): ~$12k (ASICs) + significant share of engines → major
@glocalinvestor MLCC: Yageo (the leading player) Power Semi : Pan Jit and $WOLF . But I’m not an expert on this 2 subjects so… I don’t own any of them yet😭
All I can say is foreign investors still lack imagination Taiwan AI supply chain can turn MLCC, PCB+ABF, power semis and glass fiber cloth into supercycles. Every ticker 20-30x. Photonics and memory not even guaranteed top 5 🤣 只能說老外還是太沒想像力了，做為AI中心的台股可以炒MLCC、PCB+ABF、功率半導體、玻璃纖維布、個個都是supercycle、檔檔都漲20-30x，photonics跟記憶體都不一定排得上Top 5呢 🤣
People ask what the next Photonics and Memory run is. Its still Photonics and Memory.
When will memory stocks keep rising? Between $MU and $SNDK, which will climb higher? Memory stocks have already surged, so how should you invest going forward? This article answers these questions by analyzing 10 branches of DRAM circumvention techniques and provides predictions for the upcoming memory wars, along with practical investment insights. If you’re interested in memory investments, this is a must-read. Full article: open.substack.com/pub/damnang2/p…
Wells Fargo expects $NBIS to go from 172 MW of active capacity in 2025 to 7.5 GW by 2030 By 2030, they expect: - 3.1 GW self-built / owned - 1.0 GW leased or smaller identified sites - 3.4 GW other / unallocated This forecast includes the full 1.2 GW expansion in Independence and a 1.2 GW site in Pennsylvania as flagship data centers At an estimated full AI factory capex of $70B per gigawatt, adding this capacity from today to 2030 would imply over $500B of infrastructure investment
Eric Glyman x @egymll
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49K Followers 7K Following Ex-doctor. I publish the AI infrastructure & supply chain intelligence you can't get anywhere else. Building @tessara_ai
袁起Hashman @Aiallmaker
19K Followers 491 Following 主权个人，全球主义。更健康更长久，轻松可持续。 Work for assets; Live by dreams. 健身锻炼/美股投资/搞懂女人，追求真理，专注赚钱。商务合作、1v1咨询加vx：hashwork。 Learning, Building, and Compounding . NFA，一言不合就拉黑
The Assembly @InTheAssembly
510K Followers 4 Following Macro analysis, market structure, and the trades nobody else is showing you. Access closed. Not financial advice.
TheOpeningMove @TheOpeningMove1
412 Followers 1K Following Markets, politics, on-chain. Every move starts with information.
Irrational Analysis @insane_analyst
43K Followers 101 Following Engineering-Driven Investment Analysis
SemiVision 🇹🇼 �... @semivision_tw
14K Followers 2K Following Expertise in Semiconductor Industry │Silicon Photonics│AI Industry│Supply Chain│ASIC│Ecosystem │Food │Travel
RocketMan @RKLBMan
13K Followers 164 Following Passionate $RKLB investor. Continuously learning about the Space economy posting things I find along the way. Also, $ASTS $SOFI $NBIS $ONDS $LUNR+ NFA. DYOD.
leki ⚔️ @mkfilko
15K Followers 682 Following With great risk comes great reward, always hunting for the next 10x - $BRUN, $IQE, $BB, $MRLN, Mirae ($025560), $SILC, $SHT
The Chairman's Ledger @ChairmansLedger
35K Followers 196 Following Ex-IB. Ex-startup founder, $100M+ exit. Former Special Operator. Defense tech, AI infra, Space, BTC, public market asymmetry.
qinbafrank @qinbafrank
150K Followers 1K Following Investor in AI、Crypto、TMT，跟踪最前沿科技趋势、野生宏观政经观察、研究全球资本流动性、周期趋势投资。记录个人学习和思考，经常出错常态掉坑爬坑。Runner🏃
Wolf Capital @wolfgangkasper
46K Followers 193 Following Hunting market asymmetries | Risk-reward obsessed | NFA
Jack Zhang @JackZhang70
470 Followers 2K Following AI Supply Chain Investor | Memory • Optics • Power • Cooling and 6G Love travel, 19th century lit, and fine art.
Saisei @SaiseiInvesting
11K Followers 348 Following AI/Defense/Semi/Thematic Investing ⚡️ Micro/Small Caps 🔎 Price Action ≠ Value | $NVDA $15.55, $ONDS $1.47, $IBRX $2.45, $AAOI $82, $PENG $38 📈
Ren @ren_stocks
47K Followers 86 Following Investing into the AI buildout ⚡️ 500% YTD Head of AI | Product Manager 10+ years DD and full thesis in https://t.co/4NlE3JczOz NFA
KawzInvests @KawzInvests
115K Followers 446 Following Research-focused. Photonics. AI. Defense. Tech. Space. Optic Supercycle. NFA DYDD
P Equity Research �... @pequityresearch
38K Followers 563 Following Research 📃 | Semiconductors | Sharing my knowledge & insights with the world 🌍 | Deep dives, research, and discord in my substack!📍| PMs Open👇
OrangeGroveCapital @OGCapital25
4K Followers 291 Following To God be the glory | Turned 80K to 1M in 4 years | Now hoping to build gen wealth | High-conviction, asymmetric picks only | I publish my process on Substack:
SemiAnalysis @SemiAnalysis_
143K Followers 28 Following
CK Capital @CKCapitalxx
85K Followers 411 Following Finding 10x plays in defense, space, tech & photonics | Co-Owner @CVresearch_ | Portfolio live on Autopilot | NFA DYOR
Macro_Lin｜市场观... @LinQingV
55K Followers 2K Following Ex-quant & PM｜AI chip design｜Semis × Capital Markets｜Not Financial Advice
Gaetano @crux_capital_
55K Followers 543 Following Photonics/Physical AI Company Coverage On Substack https://t.co/6T2naqbkXV
Ehrmantraut Capital @EhrmantrautCap_
16K Followers 146 Following Investor from the Netherlands | AI, memory, photonics and space. Not financial advice.
Nutty @NuttyCLD
20K Followers 2K Following Analog IC Designer | Structural Approach in the AI Wave | Semiconductors & Power & Optics | https://t.co/wL7xmQ1wVu
dylan ツ @demian_ai
21K Followers 2K Following GTM for @nebiustf @nebiusai // ex @Scaleway // from silicon to token, inference and anything in between. Views are my own - not financial advice
Deepvue @Deepvue
36K Followers 80 Following Built for traders who read the tape. Charts · Screener · Watchlists · AI Terminal — one subscription, live. ★ 4.9/5 from 589+traders.
Ted Zhang @TedHZhang
68K Followers 702 Following Portfolio Manager of Turbotection® building @RevereAsset Management. Client/Business inquiries: [email protected]. Partnered with @Deepvue @TraderLion.
Connor Bates @ConnorJBates_
53K Followers 808 Following Portfolio Manager of TURBOTECTION at @RevereAsset Management. Business inquiries ~ [email protected]
Photon Capital @PhotonCap
60K Followers 2K Following Seeing tech through a different wavelength. Photonics & semiconductor research
Daniel Romero @HyperTechInvest
43K Followers 324 Following I share high-conviction tech stock picks across semis, data centers, and AI Join 14,000+ readers at https://t.co/poHmmwvC5M
PhotonBull @PhotonBull
45K Followers 2K Following Photonics Defense Space AI Busines: [email protected]
Jorge A. @InvestWithJorge
12K Followers 236 Following Finance | Personalized 1:1 Coaching / En Español También | Book Your Session: https://t.co/tNvWGeT28O
hansolar @hansolar21
31K Followers 4K Following 🔮🐶🔮 I like data, defi and degening | 💬 https://t.co/boLwACvoUeYou might like
