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AXTI和SHMD的瓶颈论点同样适用于上市公司。
@retail_mourinho 谢谢！希望你度过了一个愉快的新年。 目前很多讨论集中在私募市场，但关于 $AXTI 或 $SHMD 的瓶颈论点同样高度适用于上市公司。
英文原文
@retail_mourinho Thanks! Hope you’ve had a great new years Lot of the discussion in around private markets but the same thesis around bottlenecks with $AXTI or $SHMD is highly relevant to public companies
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AI与机器人领域迎来拐点，软体机器人商业化加速，应用前景广阔。
AI 的情况也是如此，我大约在 8 年前发表过几篇关于深度学习(Deep Learning)的基础论文，但当时并未预期很快能看到商业化落地。直到 ChatGPT 横空出世。机器人领域也是如此，我的一位同事在斯坦福大学攻读该领域的博士学位，随后获得 DARPA 资助，致力于研发聚合物肌肉纤维(Polymer Muscle Fibers)和模仿生物运动的机器人。据我所知，作为行业先驱之一，他们已在此领域耕耘了 6-7 年，但直到去年才真正加速发展。总体而言，过去一年是 AI/机器人领域的拐点，随着大语言模型(LLMs)赋予其“大脑”，软体机器人(Soft Robotics)（如 Optimus 等）正逐渐变为可商业化的现实。因此，赋予机器人更多类人动作/灵活性是下一步自然演进。其在国防及其他垂直领域的应用前景广阔。
英文原文
Same with AI ig, I published a few of the few fundamental deep learning papers like 8y ago, but didn't really expect to see anything commercialization for awhile. Then ChatGPT popped up out of nowhere. I'd say same with robotics, one of my colleagues did his stanford phd in this area and went out to get darpa funding to build out polymer muscle fibers and robots that mimic human creature movements. Pretty sure they've been building it for the past 6-7 years or so as one of the pioneers of the industry but it only really picked up steam this past year. But in general past year has really been the inflection point for ai/robotics and we're finally seeing soft robotics become more of a commercializable reality (eg. extension of optimus and others) with LLMs giving it a brain. So the next natural evolution is giving robotics more life-like movements/flexibility. It's really an open book for applications to defense and other verticals.
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难以直接投资机器人板块，因多为巨头部门或涉及地缘政治风险。
很难直接投资有趣的机器人公司，因为它们只是 $AMZN、$TSLA、现代 KRX: 005380（波士顿动力）以及你们那些巨头公司的部门。 其他有趣的可能像 $XPEV 这样是外国公司（但我不投资那些推进地缘政治对手军事相关 AI/机器人项目的公司）。 有些像 $RR 这样的公司，但它们远不是我正在关注的垂直领域类型。
英文原文
It's hard to get direct exposure to interesting robotics companies since they're just divisions in $AMZN, $TSLA, Hyundai KRX: 005380 (Boston Dynamics), and your massive companies. Other interesting ones might be foreign like $XPEV (but I don't invest in companies that advance geopolitical enemy AI/Robotic programs that relate to military). There's some like $RR, but they're nowhere close to the types of verticals I'm looking at.
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看好能取代人力、实现端到端自主运营的AI，而非仅改善利润率的辅助型AI。
我认为 $LMND 式的 AI 只是公司试图改善利润率(Margins)的副产品。我更关注基础层面的东西，即 $PLTR 代表的下一次进化，它能真正取代整个人类劳动力。以编程为例（Cursor, Windsurf），它们很棒但仍仅作为辅助工具。我们尚未实现向零监督(Zero-supervision) AI 的飞跃。例如在商务拓展(BD)中，每家公司都需要大量的外联和沟通循环。技术已存在，但依然过于碎片化。目标是建立一个可扩展的自主系统，无需人工介入，端到端地处理从外联到客户成交的整个流程。如果有私营或上市公司能做到这一点，我认为其价值将不可估量。
英文原文
I see $LMND-style AI as a byproduct of companies just trying to improve their margins. I'm more interested in the foundational stuff, next evolution $PLTR, that actually replaces the entire human workforce. For coding, as an example (Cursor, Windsurf), they're great but still used as assistance. We're not quite at the jump to zero-supervision AI. For example, in BD, you have a massive loop of outreach and communication that every company needs. The tech is there, but it’s still too fragmented. The goal is a scalable, autonomous system that handles the entire pipeline from outreach, to customer closing across every segment, end-to-end without a human in the loop. And if a private company/public company can make that happen, then I see that being incredibly valuable.
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计划周末深入研究SHMD，看好其受益于玻璃基板生产启动。
@pennycheck 谢谢，这非常有趣。本周末我将深入研究 $SHMD。如果玻璃基板（Glass Substrate）生产周期启动，他们将受益匪浅。
英文原文
@pennycheck Thanks, that's incredibly interesting. I'll take a deeper dive into $SHMD over the weekend. They would benefit a ton from the start of glass substrate production cycles.
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SHMD被视为玻璃基板领域的ASML，需深入研究。
@LucasFdez87 @pennycheck $SHMD 让我想起晶圆制造周期中的 $ASML，但玻璃基板领域也会出现类似情况。据悉，他们提供70%的基板生产设备，并很可能纳入英特尔、三星等公司的路线图。非常有趣，我需要深入研究一下。
英文原文
@LucasFdez87 @pennycheck $SHMD reminds me of $ASML for the Fab cycle, but there's going to be one for glass substrates. Apparently they provide 70% of the equipment for substrate production, and are likely part of Intel, Samsung, and other roadmaps. Very interesting, I need to look into it
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对比JTNC与AbsoLics，看好后者在CPO及玻璃基板领域的先发优势。
是 $JTNC 吗？很难想到多少纯概念股，但它们做的是 TGV，而非用于 $NVDA 共封装光学(CPO) 的光学方案，如康宁/AbsoLics。我原本更关注 CPO 方面，但还有其他机会。AbsoLics 似乎已是明确的首选，因为其市值仅 25 亿美元，获得美国 CHIPS 法案政府资助，且率先上市。尽管背负沉重包袱，但鉴于其体量较小，其玻璃基板垂直整合业务应能强力带动整体表现。
英文原文
Is it JTNC? Can't really think of many pure-play, but they're for TGV, not optical used for $NVDA CPO like Corning/Absolics. I was more focused on CPO aspect but there's other opportunities. Absolics seems like the clear favorite already since it's $2.5B MC, funded by US Gov from CHIPS, and first-to market. It does carry heavy baggage, but its glass substrate vertical should just hard-carry the rest given the small size.
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2026年十大主题投资：聚焦AI供应链瓶颈、软体机器人及支付颠覆。
2026年通讯。 主题投资：演进、颠覆与瓶颈 1. 软体机器人 - 向 $TSLA、$ONDS、波士顿动力演进。 2. 硅光子(SiPh) - 磷化铟(InP)瓶颈 | $AXTI、$LITE、$GOOGL 3. 玻璃基板 - 瓶颈 | $NVDA、$INTC、$TSM 4. 资金流动 - 对 $V、Stripe、$BOA 的颠覆 5. AI云层级 - 瓶颈 | $NBIS、$IREN、$HUT 6. LLM网络安全 - 向 $CRWD、$CSCO、$MSFT 演进 7. 低轨(LEO)太空基础设施 | 向 $RKLB、SpaceX、$ASTS 演进 8. 消费者代理工作流(50步) - 对消费者劳动力的颠覆，来自Manus、$PATH Cognition 9. 分布式计算延迟 - 瓶颈 | $TSLA、$AMZN、$GOOGL 10. 铜互连寿命延长 - 瓶颈 | $NVDA (LPU/Groq)、$AMD、$INTC _这是我对从公开信息综合及瓶颈二/三阶效应来看最感兴趣的主题投资的简要概述！_ 1. 软体机器人：向机器人的演进 传统机器人（Optimus、波士顿动力）依赖逆运动学控制刚性关节。软体机器人改变了数学模型。 我们已到达硬件（Optimus、波士顿动力、Figure）与LLM（Gemini、Grok、Opus）相遇的节点，正处于大规模商业化的开端。 通过使用受章鱼触手和人类皮肤启发的材料，机器人正从齿轮转向流体性，以处理极其精细的任务，如像人手一样处理农产品，或为 $ONDS/Andruil 无人机添加类章鱼延伸以拾取极重表面。 这种演进在于跳出思维定势思考机器人能做什么。我记得7年前曾与该领域的斯坦福博士合作，AI在多年研究后开始商业化，因此该领域也是如此。 将类生物流体性添加到刚性机器人中的可能性是无限的，这只是自然演进。 大多数可能是私人公司。 2. 硅光子 - AI基础设施的瓶颈“磷化铟(InP)卡脖子点” 从Blackwell Ultra集群到Google TPU已触及上限，需要光子互连 | 共封装光学(OCS)以实现扩展。 基板：$AXTI（通过Tongmei）和住友（日本）控制全球约60-70%的InP基板市场。 材料：Vital Materials（中国）和AXT等公司控制原材料铟本身的精炼（78%+的供应链）。 如果你是美国科技巨头，你2026年的整个“AI增长故事”取决于由地缘政治对手控制的材料。 唯一可扩展的解决方案是工程绕行，要么实现芯片上光传输，同时减少90%的铟使用，要么使用微小的磷化铟薄片代替大型昂贵晶圆。 瓶颈本身有机会，如AXT、住友。或帮助解决它的公司如 $POET。 3. 玻璃基板 - 解决从 $NVDA 到其他公司的共封装光学(CPO)瓶颈 向玻璃基板的转变本质上是半导体行业对当前材料物理极限的回答。 当前芯片位于有机材料（本质上是专用塑料）制成的基板上。随着芯片变大，如Nvidia巨大的GPU封装，塑料基板会翘曲。 因此，玻璃基板正成为共封装光学(CPO)的行业标准，因为它们解决了光子学中最大的对齐问题。 美国政府已视其为必要，我们看到巨额补贴流向这些公司。 $INTC、三星电子、Absolics（SKC子公司）、DNP等是主要受益者，尤其是随着MRVL和 $AVGO（推动光学开关的玻璃）推进CPO革命。 4. 资金流动 - 对卡网络、银行、交易所和支付的颠覆 几十年来，资金转移一直是“收费公路”业务。每次刷卡，2%到3%的钱流入卡网络（Visa/Mastercard）和发卡行的口袋。 或者从交易所买卖加密货币是0.2-1%。这是历史上最有利可图、“不可杀死”的商业模式。 直到现在。2025年的“天才法案”刚刚将金钱传输许可证或银行特许状交给像 $XRP 这样的公司，赋予了他们王国钥匙。 对我来说并非理论。我恰好正在自己的初创公司与创建V / $PYPL 实时支付网络的人一起从事这项工作。 但基本上，拥有现有MTL或追求银行特许状并利用天才法案及其他技术的公司，现在可以通过在美联储和区块链之上进行结算来绕过传统百分比费用，有效地将基于百分比的费用转化为几美分。 99%的公司会这样做吗？可能不会，因为支付行业的所有利润率都将归零。但我乐意看到。 但基本上，Stripe以11亿美元收购Bridge本应是对现有公司的红色警报，表明1天ACH、 interchange模式、25美元国际转账的日子即将结束。 这扩展到许多其他相邻领域，从低费用颠覆者如 $HOOD、Mercury，一直到稳定币新银行，或制作自己稳定币的公司如 $SOFI。 5. AI云层级 - 超大规模计算瓶颈的解决方案 当超大规模云厂商被困在3-5年的电网互连队列中时，像WULF和IREN这样的矿工今天就拥有即插即用的GW级算力。 这是千载难逢的机会，超大规模云厂商将其现金牛云收入流向小公司。 这里有不同的层级，从Fluidstack、Poolside、Fireworks在GPU编排层，到IREN等公司构建的裸金属层。 然后有成为超大规模云厂商本身，如NBIS拥有物理位置、GPU、软件编排，然后为推理提供简单接口。 这是少数小公司在未来一两年成为AWS或Azure，或被收购（例如GOOGL以47亿美元收购Intersect）的机会。 像NBIS、IREN、CRWV这样的新云，以及像CIFR、WULF、HUT这样的colo玩家（以及私人部门->能源）将受益。 6. LLM网络安全 - 向现代安全和漏洞防御的演进 最近的报告（例如来自Anthropic红队）显示，高级模型如Opus（及未来版本）可以自主扫描开源智能合约，并在几分钟内识别价值数百万美元的“零日”漏洞。 含义：如果AI能在不可变的区块链合约中找到逻辑缺陷，它也能在银行的SWIFT API或电网控制软件中找到缺陷。 同样适用于KYC/AML。像Gemini Nano Banana这样的模型能够创建逼真的图像/视频，人们能够绕过许多程序。 这个领域有很多不性感但具Alpha潜力的事情，如LLM自动化SOC2/PCI DSS合规，代理坐在服务器上，持续监控日志，并自动生成审计所需的证据。 7. 低轨(LEO)太空基础设施 | 向拓展最后疆域的演进 太空是下一个大事情。这并不新鲜。（希望你懂这个笑话）。但从像 $RKLB、SpaceX这样的公司，到解决轨道拥堵或发射节奏瓶颈的公司，再到像ASTS或Starlink这样商业化基础设施的公司，在未来一年呈现许多机会。 因此，像Impulse、Blue Origin、$ASOZF到RKLB、$ASTS这样的公司将受益于整个链条。 8. 消费者代理工作流(50步) - 对消费者劳动力的颠覆，来自Manus、PATH Cognition 这一点很简单，无需解释。但在对就业+成本节约的潜在影响上显而易见。 你如何自动化商务拓展？如何自动化营销？如何自动化软件工程师？ 这超越了ChatGPT的几步回答，直接进入现实世界，AI代理可以在X上漫游，找到合适的人，发送DM，继续对话，并在一个工作流中导致销售电话。 这是“聊天机器人”时代的结束和“行动”时代的开始，取代公司以前需要的所有人。 我尚未看到任何公司大规模做到这一点。拥有这些的公共公司如META并没有呈现最佳敞口。也许是 $PATH 在公共空间。 9. 分布式计算延迟 - 解决AI计算容量紧张的瓶颈 像GOOGL Cloud、MSFT Azure这样的超大规模云厂商已达最大容量。 Elon Musk已经提出分布式计算作为解决此问题的未来（例如，拥有 $TSLA 网络为LLM推理提供计算）。 “Tesla计算云”论点很迷人，但我识别出的最大物理障碍是：推理延迟。 要生成“Token B”，模型必须先生成“Token A”。它不能同时做两者。如果你将一个巨大模型（如Grok-3）拆分到5辆不同的汽车中以适应内存，你必须为每个生成的Token在这些汽车之间发送数据。 因此，如果汽车之间的网络延迟甚至是20ms（5G的乐观估计），而你生成50个Token，你刚刚在计算时间之上添加了1秒的纯“等待时间”（延迟）。在使用NVLink的数据中心中，该等待时间以纳秒计。 同样适用于零售用户拥有的任何备用计算机、GPU等。有数十亿消费级GPU（Teslas、iPhones、游戏PC）90%的时间闲置。 解决推理的“分布式延迟”问题呈现了计算史上最大的套利机会之一。 尚未看到任何公司大规模完成此任务。也许NVIDIA Dynamo、$AKAM、TSLA正在接近。 10. 铜互连寿命延长 - 解决Nvidia和其他公司的瓶颈 既然我们不能拥有无限的InP，我们必须用现有的东西（例如铜）进行工程绕行，所以铜电缆可以做物理上说它不应该做的事，如在不损失信号的情况下跨机架传输224G信号。 行业在InP上遇到了硬性停止，美国在物理上无法开采和精炼足够的InP将数据中心中的每个链接变成光纤。 如果有任何帮助，那就是好事。例如，NVDA对Groq团队和IP的200亿美元“收购雇佣”。LPU更多是关于推理延迟/架构，但它作为副产品解决了铜寿命延长。Groq的整个架构在延迟上击败了Nvidia，因为它拒绝了光学。Groq使用“确定性”网格，依赖芯片之间的直接电气（铜）连接，避免光学开关的“抖动”和转换时间。 像 $ALAB、$CRDO、Groq，或任何能找到用铜绕过光学瓶颈方法的公司将是赢家。 _这里有从私人部门投资到公共部门的众多交易！只是今天即兴写下了我的想法，但乐意稍后详细阐述。 无论如何，我相信这些主题投资中的许多： 从投资InQ瓶颈绕行（$POET）或瓶颈本身（$AXTI）到公共部门的颠覆者（$CRCL）。 到投资铜扩展瓶颈修复（Groq）、银行特许状颠覆者（Mercury）到私人部门的演进公司（Lightmatter、Festo）。 在2026年呈现不对称上行空间。 新年快乐！
英文原文
2026 Newsletter. Thematic Investments: Evolution, Disruption, and Bottlenecks 1. Soft Robotics - Evolution to $TSLA, $ONDS, Boston Dynamics. 2. SiPh - InP Bottleneck | $AXTI, $LITE, $GOOGL 3. Glass Substrates - Bottleneck | $NVDA, $INTC, $TSM 4. Money Movement - Disruption to $V, Stripe, $BOA 5. AI Cloud Layers - Bottleneck | $NBIS, $IREN, $HUT. 6. LLM Cybersecuirty - Evolution to $CRWD, $CSCO, $MSFT 7. LEO Space Infrastructure | Evolution to $RKLB, SpaceX, $ASTS 8. Consumer Agentic Workflows (50 Step) - Disruption to the Consumer Workforce, from Manus, $PATH Cognition 9. Distributed Computing Latency - Bottleneck | $TSLA, $AMZN, $GOOGL, 10. Copper Interconnect Life Extension - Bottleneck | $NVDA (LPU/Groq), $AMD, $INTC _ This is an light overview of thematic investments I find the most interesting from a public-information synthesis perspective + second/third-order effects from bottlenecks! _ 1. Soft Robotics: The Evolution to Robotics Traditional robotics (Optimus, Boston Dynamics) relies on Inverse Kinematics to rigid joints. Soft robotics changes the math. We've met the point where hardware (Optimus, Boston Dynamics, Figure) met LLMs (Gemini, Grok, Opus), and we're at the beginning of possible widespread commercialization. By using materials inspired by octopus tentacles and human skin, robots are moving away from gears and toward fluidity to handle extremely delicate tasks like handling produce like the human hand, to picking up extremely heavy surfaces adding Octopus-like extensions to $ONDS/Andruil Drones. The evolution is thinking outside the box in terms of what robotics can do. I remember working with some Stanford PHds in this field like 7 years ago, and it just so happens AI is starting to be commercialized after many years of research. So expected, this field to be as well. Possibilities are limitless adding organism-like fluidity to rigid robotics, this is just the natural evolution. Most of these are prob private companies. _ 2. Silicon Photonics - Bottleneck of the AI Infrastructure "InP Chokepoint" Blackwell Ultra Clusters to Google TPUs have hit the upper wall and requires photonics for interconnects | OCS to scale up. The Substrates: $AXTI (via Tongmei) and Sumitomo (Japan) control roughly 60-70% of the world's InP substrate market. The Materials: Companies like Vital Materials (China) and AXT control the refining of the raw Indium itself (78%+ of supply chain). If you are a US tech giant, your entire "AI Growth Story" for 2026 depends on materials controlled by geopolitical rivals. The only scalable solution is engineering around it, either by delivering light-on-chip, while using 90% less InP or companies that use tiny slivers of Indium Phosphide instead of large, expensive wafers. There's opportunities with the bottleneck itself like AXT, Sumitomo. Or companies that help address it like $POET. _ 3. Glass Substrates - Fixing the Bottleneck for CPOs from $NVDA to others. The shift toward glass substrates is essentially the semiconductor industry’s answer to a physical wall they are hitting with current materials. Current chips sit on a substrate made of organic materials (essentially specialized plastic). As chips get larger, like Nvidia's massive GPU packages, plastic substrates warps. So, glass substrates is becoming the industry standard for Co-Packaged Optics (CPO) because they solve the single biggest problem in photonics with alignment. US Government already sees this as a necessity and we've seen huge subsidies funneling down to some of these companies. Companies like $INTC, Samsung Electronics, Absolics (SKC Subsidiary), DNP, and others are the main beneficiaries, especially as MRVL and $AVGO (driving glass for optical switches) move forward with CPO revolution. _ 4. Money Movement - The Disruption to Card Networks, Banking, Exchange, and Payments For decades, moving money has been a "toll road" business. Every time you swiped a card, 2% to 3% of that money vanished into the pockets of the Card Networks (Visa/Mastercard) and Issuing Banks. Or buying/selling crypto from an exchange would be .2-1%. It was the most profitable, "un-killable" business model in history. Until now. The "Genius Act" of 2025 just handed companies like $XRP with Money Transmitter Licenses or Banking Charters the keys to the kingdom. Not really theoretical for me. I happen to be working on this myself at my own startup with some folks who created V / $PYPL's real-time payment networks. But basically companies with existing MTLs or pursuing banking charters leveraging the Genius Act and some other tech can now bypass legacy % fees by doing settlement on top of the Federal Reserve and blockchains, effectively converting percentage-based fees into a few cents. Would 99% companies do it? Probably not since every single margin from across the payment industry would just go to 0. I'd be happy though. But basically Bridge's $1.1B acquisition by Stripe should have been a red-alarm to existing companies that days of 1-Day ACH, interchange models, $25 international transfers, are soon to be over. This extends to many other adjacents from low fee disruptions like $HOOD, Mercury all the way to Stablecoin Neobanks, or companies making their own stablecoins like $SOFI. _ 5. AI Cloud Layers - The Solution to HyperScaler compute Bottleneck While Hyperscalers are stuck in 3-5 year grid interconnection queues, miners like WULF and IREN are sitting on plug-ready GWs today This is the opportunity of a lifetime as hyperscaler funnel their cash cow Cloud revenues down to tiny companies. There's many different layers to this from Fluidstack, Poolside, Fireworks on the GPU orchestration layer, to the bare metal layer that companies like IREN are building. Then there's becoming the hyperscaler themselves like NBIS owning the physical locations, the GPU, software orchestration, and then providing simple interfaces for inference. This is the opportunity for a few small companies to become Amazon Web Service or Microsoft Azure over the next year or two, or get acquired (eg. GOOGL buying Intersect for $4.7B) Neoclouds like NBIS, IREN, CRWV, down to colo plays like CIFR, WULF, HUT (and private sectors -> Energy) stand to benefit. _ 6. LLM Cybersecurity - The Evolution to Modern Security and Vulnerability Defense Recent reports (e.g., from Anthropic's Red Team) showed that advanced models like Opus (and future iterations) could autonomously scan open-source smart contracts and identify "Zero-Day" exploits worth millions of dollars in minutes. The Implication: If an AI can find a logic flaw in a immutable Blockchain contract, it can find a flaw in a bank's SWIFT API or a power grid's control software. Same with KYC/AML. Models like Gemini Nano Banana are able to create realistic images/videos of people and people are able to get past a lot of programs. There's tons of things as an unsexy alpha in this field like LLMs automating away SOC2/PCI dss compliance to agents sitting on a server, continuously monitor logs, and auto-generate the evidence needed for auditors. 7. LEO Space Infrastructure | The Evolution to Expanding into the final frontier. Space is the next big thing. This is not anything new. (hope you got the joke). But anywhere from companies like $RKLB, SpaceX. Companies that fix orbital congestion or launch cadence bottlenecks. To companies that commercialize the infrastructure like ASTS or Starlink present many opportunities over the next year. So companies like Impulse, Blue Origin, $ASOZF to RKLB, $ASTS stand to benefit across the entire chain. 8. Consumer Agentic Workflows (50 Step) - Disruption to the Consumer Workforce, from Manus, PATH Cognition This one is simple and needs no explanation. But largely obvious in potential impact on employment + cost saving. How do you automate away business development? How do you automate away marketing? How do you automate away software engineers? This is going past few step ChatGPT answers and directly in to the real world where an AI agent can roam X, find the right people, DM someone, continue conversations, and lead to a sales call in just one workflow. This is the end of the "Chatbot" era and the beginning of the "Action" era replacing everyone previously required in a company. I haven't quite seen this done at scale yet with any company. Public companies like META that own these, don't really present the best exposure. Maybe $PATH for public space. 9. Distributed Computing Latency - Fixing the Bottleneck for AI Compute Capacity Strains Hyperscalers like GOOGL Cloud, MSFT Azure at max capacity. Elon Musk already floated distributed computing as the future of solving this issue (eg. having networks of $TSLA's providing compute for LLMs for inference). The "Tesla Compute Cloud" thesis is fascinating, but the single biggest physical barrier I've identified is: Inference Latency. Too generate "Token B," the model must first finish generating "Token A." It cannot do both at the same time. If you split a massive model (like Grok-3) across 5 different cars to fit it in memory, you have to send data between those cars for every single token generated. So, if your network latency between cars is even 20ms (optimistic for 5G), and you are generating 50 tokens, you just added 1 full second of pure "waiting time" (latency) on top of the compute time. In a data center using NVLink, that wait time is measured in nanoseconds. Same applies to any spare computer, GPU, and others owned by retail users. And there's billions of consumer GPUs (Teslas, iPhones, Gaming PCs) that sit idle 90% of the time. Solving the "distributed latency" problem for inference presents one of the single greatest arbitrage opportunity in the history of computing. Haven't really seen any companies that accomplished this at scale yet. Maybe NVIDIA Dynamo, $AKAM, TSLA, getting a little closer. 10. Copper Interconnect Life Extension - Addressing the Bottlenecks of Nvidia and Others Since we can't have infinite InP, we have to engineer around it with what we have (eg. Copper), so copper cables can do things that physics said it shouldnt like carrying 224G signals across a rack without signal loss. The industry is hitting a hard stop on InP where, US cannot physically cannot mine and refine enough InP to turn every link in a data center into fiber optics. If anything helps, then it's good. EG. NVDA's $20B "Acqui-hire" of Groq's team and IP. LPU is more about inference latency/architecture but it addresses copper life extension as a byproduct. Groq’s entire architecture beat Nvidia on latency because it rejected optics. Groq uses a "deterministic" mesh that relies on direct electrical (copper) connections between chips, avoiding the "jitter" and conversion time of optical switches. Companies like $ALAB, $CRDO, Groq, or anyone who can find ways to engineer around the optical bottleneck with copper will be a winner. _ There are tons of trades from both private sector investments to public! Just wrote up my thoughts on the fly today, but happy to elaborate later. Regardless I believe a lot of these thematic investments from: Investing in InQ Bottleneck Workarounds ( $POET ) or the bottleneck itself ( $AXTI ) to Disruptors ( $CRCL ) in the public sector. To Investing in copper extension bottleneck fixes (Groq), bank charter disruptors (Mercury) to evolutionary companies (Lightmatter, Festo) in the private sector. Present asymmetrical upside in 2026. Happy New Year!
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博主新年祝福，期待税务操作后市场动向及继续图表分析。
@soulbiri1 新年快乐！税务亏损收割(Tax harvesting)应该已经完成了，所以我很期待看到哪些股票会率先启动。让我们继续新一年的动漫风格图表分析(anime charting)吧。
英文原文
@soulbiri1 Happy new year! Tax harvesting should be done so I’m excited to see what stocks ends up moving first. Here’s to another year of anime charting
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博主新年祝福，虽对部分标的表现遗憾，但看好2026年前景。
@Neat_Lama @soulbiri1 @platochi 新年快乐，后辈们！虽然 weebius 和 neoclouds 并非一切如计划般顺利，让我感到难过，但我相信 2026 年将是一个光明的年份！
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@Neat_Lama @soulbiri1 @platochi Happy new year kohai! I’m still sad not everything worked as planned with weebius and neoclouds but i believe 2026 will be a bright year!
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分享期权交易策略：为减少时间价值衰减，选择远期合约布局财报季。
@jliljliljlil 我的意思是，我个人今天投入了几十万美元购买这些短期到期的看涨期权(Short-dated Calls)。 我假设1月初至2月财报季(Earnings)会到来，所以我布局了像9月这样更远期到期的合约，以减少时间价值衰减(Theta Decay) https://t.co/LqLx4lETNL
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@jliljliljlil I mean I personally threw a few hundred thousand on shorter dated calls like these today. I’d assume early Jan-Feb when earnings come out so I did stuff later on in the year like Sept so there’s less theta decay https://t.co/LqLx4lETNL
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博主质问对方此前关于以太坊年底价格的预测。
@fundstrat 你几个月前不是说2025年底以太坊(Ethereum)会到$16K吗？ https://t.co/7z28xJvwng
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@fundstrat Didn't you say $16K Ethereum end of year 2025 a few months ago? https://t.co/7z28xJvwng
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NBIS波动极大，但看好其2026年跑赢大盘并扭转趋势。
$NBIS 目前基本上是一家由 5 家不同公司组成的实体，也是整个市场中波动率（Beta）最高的股票之一。 不过其波动性极大。其同行如 $CRWV 前阵子单日涨幅曾达 24%。 Nebius 在最初达成 $MSFT 交易时单日也曾上涨 40%。且在两周内经历了 100->140->100 的完整轮回。所以波动性极强，绝对不适合心脏脆弱者。 抛开这点，我对 Nebius 在 2026 年跑赢大盘并扭转当前趋势极具信心。这基本上等同于投资一家 FSD 级别的 L4 级自动驾驶出租车公司、一家几乎被所有财富 500 强使用的数据库，以及其他同比增长三位数并将更多利润反哺核心业务的业务板块。
英文原文
$NBIS is basically 5 different companies in 1 at this point and one of the most high-beta stocks in the entire market. Extremely volatile though. It's peers like $CRWV went up 24% in a day the other week off. Nebius also went up 40% in a day on the initial $MSFT deal. And round tripped 100->140 -> 100 all in the span of two weeks. So just extremely volatile and definitely not a stock for the faint of heart. That aside I'm extremely confident in Nebius outperforming in 2026 and reversing current trends. It's basically investing in a FSD-level 4 Robotaxi company, a DB that almost every fortune 500 company uses, and other moving parts growing triple digits Y/Y that derive more profit to the core business.
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分析$PINS高毛利与营收增长，评估研发支出合理性。
我喜欢人们分享他们的高确信度观点。$PINS 很有趣，我完全忘了它的存在。这绝对是一个不错的候选标的，过去6个月下跌了28%。我看好的一点是，他们在季度营收超过10亿美元的情况下，毛利率约为79.8%，且营收同比仍增长16%。仅从高层概览来看：研发(R&D)支出：3.71亿美元；销售与营销(S&M)支出：2.97亿美元；一般及行政(G&A)费用：1.1亿美元。可能还需要花更多时间进行建模，看看那占营收35%的研发支出是否属于浪费性的冗余。但除此之外，2.97亿美元的营销支出是合理的。
英文原文
I love it when people share their high conviction. $PINS is interesting, I completely forgot that existed. Definitely a good candidate, down -28% last 6 months. What I like is that they have ~79.8% gross margins off $1B+ quarterly revenue, and still have 16% Y/Y revenue growth. Just looking at a high-level overview: R&D: $371M Sales & marketing: $297M G&A: $110M Will probably need to spend some more time to do modelling and see if that 35% of revenue on R&D is wasteful bloat. But otherwise the $297M marketing makes sense.
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分析NVDA、SMCI等超跌股数据，认为其均值回归潜力大。
我也可以为 $NVDA 提出看空论点，即超大规模云服务商正在自研 ASIC。但在这种情况下，你要看数据而非叙事，比如英伟达的积压订单(backlog)，简直惊人。 $SMCI 远不及此，这也是它三个月下跌 40% 的原因，但它营收同比(Y/Y)仍增长 50%+，甚至将全年指引从 330 亿美元上调至 360 亿美元。 至于 $SNAP，如果你是价值投资者，它被严重低估。Perplexity 交易增加 4 亿美元价值，加上内存变现的净自由现金流(FCF)为 +6.3 亿美元。然而它年初至今仍下跌 25%。 $HIMS 完成了 Zava 收购并拓展欧洲。亚马逊等虽已推出竞品，但我认为 Hims 明年营收同比(Y/Y)仍将增长 20%+，且有回购。它们显然不像 SK 海力士、台积电、Lite-On 等神级股票那样以疯狂速度增长，但超跌+均值回归速度更快，上行空间可能更大。
英文原文
So I can argue the bear case for $NVDA too, that hyperscalers are building their own ASICs. But in cases like these, you look at numbers rather than narrative like, the backlog for Nvidia, it's wild. $SMCI is nowhere close, which is why it's down 40% in 3 months but it's still growing revenue 50%+ Y/Y and even raised FY guidance from $33B to FY $36B. As for $SNAP, it's materially undervalued if you're value investing. Perplexity deal adding $400m in value, then net FCF from memory monetization is +$630M. Yet it's trading 25% down from the start of the year. $HIMS has Zava acqusition and is expanding to Europe. Amazon and others already launched competitors but Hims I think is still growing 20%+ Y/Y into next year and has buybacks. They're obviously not god-tier stocks like Sk Hynix, TSM, Lite, and others just growing at insane rates but this is the point of being over-sold + revert to mean a lot faster. And upside could be higher.
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谷歌股价波动源于情绪反转，预计Marvell也会如此。
@FinansMerak137 @SaItinerant 是的，我在 $GOOGL 跌至 140 美元时做多。基本面实际上没有任何变化，这纯粹是基于对搜索业务措辞解读的情绪反转。 我预计 Marvell 也会随机出现同样的情况。
英文原文
@FinansMerak137 @SaItinerant Yeah I was long $GOOGL on the drop to $140. Literally nothing changed about the business, it was just a sentiment flip just based on how you worded search. I'd probably expect the same with Marvell randomly.
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利用年末税务收割导致的超跌，寻找基本面完好标的博取一月效应反弹。
新年快乐！ 最奇怪的“季节性异常”是均值回归反弹的“一月效应”。 寻找那些被严重抛售的股票，例如在3个月图表上跌幅分别为-53.9%的 $MSTR、-41.9%的 $HIMS 或 -38.63%的 $SMCI，或者像年初至今(YTD)下跌-25%的 $SNAP 和下跌-23%的 $MRVL 这样的公司。 主要候选标的拥有强劲的未来盈利预期，但股价却大幅下跌，例如Snapchat（涉及Perplexity、记忆变现等概念）、SMCI（未来同比增长50%以上，但因Q1至Q2积压订单延迟而下跌），或Marvell（未来Maia系列将带来三位数的收入增长）。 年末税务收割(Tax Harvesting)正在市场全面生效，这造成了人为的下行压力。 因此，当你在寻找新年折扣时，务必检查基本面是否真的没有大问题。（例如，MSTR因面临MSCI除名风险而风险更高） 但历史上，如果这些股票因年末税务亏损而加速抛售，它们往往会在年初第一个月率先上涨。这一趋势为交易者提供了最具回报的机会之一。
英文原文
Happy New Year! The strangest “seasonal anomaly" is the January Effect for mean reversion rallies. Look for beaten down names like $MSTR -53.9%, $HIMS -41.9%, or $SMCI -38.63% on 3M charts or companies like $SNAP -25% YTD, $MRVL -23% YTD The primary candidates have strong forward earnings, but are down like Snapchat (perplexity, memory monetization), SMCI (50%+ forward y/y growth but dropped on q1->q2 backlog delay), or Marvell with triple digit revenue growth from Maia down the road. The EoY tax harvesting is in full effect for the markets and this causes artificial downward pressure. So, while you’re shopping for new year discounts, make sure to check there isn’t something fundamentally too broken. (Eg. MSTR is more risky because of MSCI delisting) But, historically, these tend to rise first month of the year if they had accelerated sell offs due to end of year tax losses. This trend presents one of the most rewarding opportunities for traders.
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驳斥分析师误判，强调Marvell在XPU设计上的营收潜力。
是的，很好的笔记。仅这一点就反驳了分析师声称 $MRVL 仅做连接业务，而将实际设计输给 Alchip 等竞争对手的说法。 机构分析师如何散布此类虚假信息，因称 Marvell 仅做连接业务而抹去 $100 亿+市值或 14-20% 的股价，引发机构研报的连锁反应，然后在上周无人关注时又说“只是开玩笑”，这太疯狂了。 “来自 XPU 设计的 2028 财年营收已相当可观”这一点也至关重要。
英文原文
Yeah great note. That alone addresses the analyst claims $MRVL was only doing connectivity while losing the actual design to a competitor like Alchip too. How institutional analysts can spread misinformation like this, wipe off $10B+ or 14-20% in stock price saying DG because of Marvell was only doing connectivity, send cascading narrator through institutional notes, then say “just kidding” when nobody is paying attention a week ago is wild. “There’s meaningful FY 2028 revenue already too from XPU designs” pretty important to read too.
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Benchmark 悄悄改口亚马逊芯片供应商，媒体误导严重。
SemiAnalysis 那就错了。Benchmark 最初声称：“我们现在有高度把握认为，该公司已将亚马逊的 Trainium3 和 4 设计输给了其台湾竞争对手 Alchip。”大量媒体对此进行了报道。但在股价下跌 14% 后，Benchmark 在无人关注时悄悄收回了该说法，基本改口称：亚马逊增加了 Alchip 作为第二设计支持供应商。媒体的框架设定糟透了。
英文原文
SemiAnalysis would be wrong then. Benchmark originally claimed: "We now have a high degree of conviction that the company has lost both Amazon's Trainium3 and 4 designs to its Taiwanese competitor, Alchip." Tons of media reported on that. But Benchmark silently walked that back after stock dropped 14%, when nobody was paying attention and basically said: Amazon added Alchip as second design support vendor. The media framing is atrocious.
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批评机构误报，认为忽略噪音后MRVL是稳健长线标的。
$POET 通过 Celestial 间接受益于 $MRVL。此外还有大量与 Maia 绑定的其他光子学玩家，如 $AAOI。 但时间线简直荒谬，让我很恼火： 12月8日：Benchmark 下调评级，声称“高度确信”Marvell 输掉了 Trainium 3/4 给 Alchip。 12月9日：股价下跌约10%，CEO 上 CNBC 称“我们没丢失任何业务”。 12月23日：Benchmark 悄悄收回说法。Marvell “保留份额”，Alchip 仅增加“额外设计支持”。 Benchmark 做出此类声明后又在压低股价后悄悄收回，简直不可思议。 The Information 也如法炮制，但他们本就因煽动性报道闻名。 如果你只看数据并忽略新闻报道的噪音，$MRVL 看起来是一个极其稳健的长线标的。
英文原文
$POET indirectly benefits from $MRVL through Celestial. Then there's tons of other photonic players tethered to Maia like $AAOI. The timeline is so stupid though, it makes me annoyed: Dec 8: Benchmark downgrades, claims "high conviction" Marvell lost Trainium 3/4 to Alchip Dec 9: Stock drops ~10%, CEO goes on CNBC saying "we didn't lose any business" Dec 23: Benchmark quietly walks it back. Marvell "retains a position," Alchip added for "additional design support" Just found it insane how Benchmark could make claims like that then silently walk it back after tanking the stock price. Then The Information does the same but they're already known for their sensational journalism. If you just look at the numbers and ignore the noise from news reporting then $MRVL looks like an extremely solid long.
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驳斥MRVL被取代谣言，指出物理限制使其无法短期切换，视抛视为买入良机。
终于深入研究了 $MRVL。 我的第一反应：市场和分析员/新闻到底在抽什么？ 来自 Maia 量产的收入几乎是 $MRVL 2025 财年总收入的两倍。 看数据，这令人震惊： 富邦 (Fubon) 预计 Marvell 仅在 2027 年微软 Maia 量产中就能获得 100-120 亿美元的收入。 作为对比，Marvell 2025 财年收入为 57.7 亿美元。 无论如何看，Marvell 看起来都是博通 (Broadcom) 的强劲竞争对手（在收购 Celestial 之后），也是一个极佳的长线标的。 但是... The Information / Benchmark 发布了新闻称： - 微软正在与博通谈判，以在未来几代产品中取代 Marvell。 - 亚马逊 Trainium 3 和 4 的设计流向了台湾竞争对手 Alchip Technologies。 导致股价大幅抛售。 深入研究后，这怎么可能呢？ “Marvell Technology 股价下跌，因为 Benchmark Equity Research 下调评级， citing 失去亚马逊 AI 芯片业务。” “Benchmark 认为 Marvell 输给了 Alchip，尽管 Marvell 最近预测数据中心增长强劲。” - Trainium 3 的生产已锁定 Marvell 的设计。如果不从头设计全新芯片，你无法“切换”到 Alchip。如果要共享范围，当然可以，但措辞简直糟糕透顶。 $MRVL 已经获得了 $AMZN 整个 2026 年的 Trainium 3 订单，亚马逊为什么要取消所有订单然后选择新供应商？ 可能有不同的范围、额外的设计服务等，但所有新闻的框架听起来像是 $MRVL 被取代了。 Benchmark 自己在造成损害后（通过更多“行业讨论”）大幅收回了说法，称 Marvell 并未完全失去 Trainium 3/4，而是亚马逊增加了 Alchip 作为额外设计支持，而 Marvell 仍保留地位。 但股价已经受损。 至于 The Information 关于 $MSFT Maia 输给博通的报道，这是煽动性新闻。 摩根大通 (JP Morgan) 后来直接出来基本说这是假的，“没有 ASIC 项目份额损失”。 在微软交易上，市场完全错过了“立即取代”和“未来几代”讨论之间的细微差别。 物理规律使得从 Maia 到博通的立即切换不可能，除非有惊人的多年延迟。 现在用博通取代 Marvell 的 Maia 300 需要废弃整个 2nm 掩模集，重新设计 I/O 环，并重启 3 年的验证周期。Marvell 特定的 SerDes IP 也嵌入在芯片的 I/O 环中（重定时器和交换机通信）。如果微软目标是在 2027 年量产 Maia 300，芯片必须在 2026 年中后期进入验证阶段。这意味着物理设计今天已经冻结或接近冻结。 今天（2025 年 12 月）与 $AVGO 的新 ASIC 设计将面临以下最短时间线： - 架构与 RTL（12 个月）：逻辑定义和 IP 选择。 - 物理设计到流片（9-12 个月） - 掩模生产与晶圆厂（3-5 个月）：TSMC 2nm 周期。 - 硅后验证（6-9 个月） 总时间：~30-38 个月。 即使加快几个月，这些“与 $AVGO 的讨论”最早也要到 2028 年才能大规模部署。为了赶上 2027 年的量产时间表，微软必须出货 Marvell 的设计。 此外，微软的机架基础设施使用 Marvell 的“Alaska”重定时器和 DSP 在电缆中。 微软必须： - 重新认证机架中每根电缆和背板的信号完整性。 - 拆下主板上的 Marvell 重定时器并替换为博通版本 还要更改他们的定制液冷机架“sidekick”，这是针对 $MRVL Maia 芯片特定版图分布指定的。$AVGO 芯片会有完全不同的物理版图和热密度，他们需要重新设计机架。 所以当市场计入立即取代风险并基于 TMZ 式的讨论八卦谣言交易时，他们完全是在开玩笑，谣言称微软想探索选项。 $MRVL CEO 甚至出面表示他们手中握有明年全年预测的“采购订单”。2026 年的预测可能偏低，因为 $AMZN 和 $MSFT 的量产都面临延迟。 但如果你看长期 Q4 2026 到 2027，仅从他们的订单和 Maia 300 来看：即使仅估计 ~200 亿美元收入带来 7.72 美元每股收益，30 倍市盈率（之前交易在 35-40 倍），如果是两年后等待，股价将从 85 美元涨到 231.60 美元/股。 所以 TLDR：整个“取代”理论被极其完全地误解了，只是噪音。通常我不会为此写整篇文章，但看到 The Information 和其他新闻一次又一次出现，通过煽动性措辞传播错误信息，这简直疯了。 中期切换在物理上是不可能的，市场误解了取代。所以最近的抛售看起来是一个坚实的买入机会。 至于未来的讨论，当然任何公司都希望供应商多元化。如果 Meta 想从博通多源采购，$MRVL 也可以对 $META MTAI 未来几代做同样的事。 如果仅按单个项目 Maia 300 建模并忽略媒体噪音，$MRVL 看起来非常有前景。
英文原文
Finally took a deeper look at $MRVL. My first reaction: What are the markets and analysts/news smoking? Revenue derived from Maia ramp is literally double $MRVL's FY 2025 revenue. If we look at the numbers, it's staggering: Marvell is modeled to take in $10-$12B in revenue (Fubon) in Microsoft Maia ramp in 2027. Alone. To put that in perspective, Marvell's FY 2025 revenue was $5.77B. No matter how I look at it, Marvell looks like a strong Broadcom competitor (after Celestial acqusition) and a terrific long. But... The Information / Benchmark released news that: - Microsoft is negotiating with Broadcom to displace Marvell in future generations. - Amazon Trainium 3 and 4 designs went to Taiwanese competitor Alchip Technologies. causing a massive selloff. After looking into it more, how is this even possible? "Marvell Technology declines after Benchmark Equity Research downgrades the stock, citing a loss of Amazon’s AI chip business." "Benchmark believes Marvell lost Amazon’s Trainium 3 and 4 designs to Alchip, despite Marvell’s recent forecast of strong data-center growth." - Trainium 3 production are locked to Marvell's design. You can't "swap" to Alchip without designing an entirely new chip from scratch. If you want to share scope, sure but the wording is absolutely atrocious. $MRVL already secured Trainium 3 orders from $AMZN throughout 2026, why in the world would Amazon cancel them all and then go with a new vendor? There can be different scope, additional design services, etc. but the framing from all the news sounds like $MRVL is being displaced. Benchmark itself later walked it back materially after causing damage (via more "industry discussions") and said Marvell did not lose Trainium 3/4 outright, but that Amazon added Alchip for additional design support while Marvell retains a position. But the damage has already been done to stock price. As for The Information report on $MSFT Maia loss to Broadcom this is sensational journalism. JP Morgan later literally came out and basically said it's all false, "there has been no ASIC program share loss". On the Microsoft deal, markets completely missed the nuance between "immediate displacement" and "future generation" discussions. The physics of this make an immediate swap impossible from Maia to Broadcom without incredible multiple-year delays. To replace Marvell with Broadcom for the Maia 300 now would require scrapping the entire 2nm mask set, redesigning the I/O ring, and restarting a 3-year validation cycle. Marvell’s specific SerDes Is is also embedded in the chip’s I/O ring (retimers and switches comms). If Microsoft targets a 2027 ramp for Maia 300, the chip must be in validation phase by mid-to-late 2026. This implies the physical design is already frozen or nearing freeze today. A new ASIC design with $AVGO today (Dec 2025) would face this minimum timeline: - Architecture & RTL (12 months): Logic definition and IP selection. - Physical Design to tape ot (9-12 months) - Mask prod & Fab (3-5 months): TSMC 2nm cycle. - Post-Silicon validation (6-9 months) Total Time: ~30–38 months. Even if this is sped up by many months, these "Discussions with $AVGO " not be ready for volume deployment until 2028 at the very earliest. To hit the 2027 ramp timelines, Microsoft must ship the Marvell design. Then there's Microsoft’s rack infrastructure uses Marvell’s "Alaska" retimers and DSPs in the cables. Microsoft would have to: - Re-qualify the signal integrity of every cable and backplane in the rack. - Rip out Marvell retimers from the motherboard and replace them with Broadcom versions As well as change their custom liquid-cooling rack "sidekick", which was specified toward specific distribution of the $MRVL Maia chip’s floorplan. A $AVGO chip would have a completely different physical floorplan and thermal density and they'd need to regineer the rack. So market are completely trolling if when they price in immediate displacement risk and trade on TMZ-style discussion gossip rumors that Microsoft wanted to explore options. $MRVL CEO even went out and said they have "purchase orders in hand" for the entirety of the next year's forecast. 2026 forecasts are probably light because $AMZN and $MSFT both faced delays on their ramp. But if you look at longer term Q4 2026 into 2027, we just from their orders and Maia 300 alone: even just estimating $7.72 EPS from ~$20.0B revenue, 30x P/E (they've traded 35-40 before), would be $231.60/share from $85 if you want to wait 2 years. So TLDR: The whole “replacement” theory is extremely completely misinterpreted and is just noise. Normally, I wouldn't write a whole post on it, but seeing The Information and other news pop up again, and again, spreading misinformation through sensational wording is just crazy. There's a physical impossibility of a mid-cycle swap and market misunderstanding of displacement. So the recent sell-off looks like a solid buying opportunity. As for future discussions, of course any company would want vendor diversification. $MRVL can go do the same with $META MTAI future generations too if Meta wants to multi-source away from Broadcom. If you model just by single project Maia 300 alone and ignore media noise, $MRVL looks incredibly promising.
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询问SKC与三星电机在CPO玻璃基板领域的看法。
@jukan05 说到三星，我很好奇你对 SKC/Absolics 和三星电机在共封装光学(CPO) 玻璃基板领域的看法，因为它们都是韩国公司。
英文原文
@jukan05 Speaking of Samsung, I’m curious what your opinion is on SKC/Absolics and Samsung Electro-mechanics on the glass substrates space for CPO, since they’re both Korean companies
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博主幽默回应互动，称与老虎是好朋友
@notthefxckinone @beauty_oe @kaltsit99__ @DeepValueBagger 不，我和老虎是好朋友
英文原文
@notthefxckinone @beauty_oe @kaltsit99__ @DeepValueBagger Nah the tiger and I are best friends
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博主分享在日本旅行时看到奇特动物的经历。
@beauty_oe @kaltsit99__ @DeepValueBagger 到目前为止我很喜欢日本！我遇到了很多奇特的动物 https://t.co/dt8RBU5Q4T
英文原文
@beauty_oe @kaltsit99__ @DeepValueBagger I love Japan so far! There’s so many exotic animals I encountered https://t.co/dt8RBU5Q4T
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博主回应地理游戏互动，确认日本地点。
@kaltsit99__ @DeepValueBagger 地理猜猜乐(Geoguessr)高手！没错，是热海、富士山、东京和和歌山。
英文原文
@kaltsit99__ @DeepValueBagger Geoguessr pro! Yeah Atami, Fuji, Tokyo, and Wakayama
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博主分享个人视角的景色图片
@DeepValueBagger 享受这景色！我也想炫耀一下我的 https://t.co/Qrsurgai2m
英文原文
@DeepValueBagger Enjoy the view! I wanted to brag about mine too https://t.co/Qrsurgai2m
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博主感谢并推荐关注另一位博主。
@crux_capital_ 谢谢，你也是值得关注的优质博主 :)。
英文原文
@crux_capital_ thx, you're a great follow as well :)
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认为英伟达因高盈利进入证明阶段
@BitcoinAIGuy 鉴于 $NVDA 的盈利能力，我认为我们正处于“证明它”的阶段，哈哈。
英文原文
@BitcoinAIGuy I think we’re a little prove it stage with $NVDA given how profitable they are lol
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论证微软向Iren预付款项的商业逻辑。
@Lazarus_Capital 我召唤 @Agrippa_Inv 作为我的第一只宝可梦，来论证 $MSFT 为 $IREN 超大规模云服务商交易进行预付款的好处。
英文原文
@Lazarus_Capital I summon @Agrippa_Inv as my first Pokémon to argue benefits of $MSFT prepayment for $IREN hyperscaler deal.
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澄清推文讨论的是IREN微软预付款计算。
@Lazarus_Capital 当然，这条推文具体是在讨论 $IREN 微软预付款的计算。
英文原文
@Lazarus_Capital Sure, so this one was specifically talking about $IREN Microsoft prepayment calculations.