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AEHR因拿下光学收发器大客户而上涨，若进入量产阶段股价或超$60
这不是贪婪，这是基本面。$AEHR价格上涨是因为他们拿下了光学收发器(optical transceivers)领域的领导者。这可能是$AVGO、$LITE、$COHR、$MRVL、$CSCO或其他公司。市场是前瞻性的。以我之见，如果它进入像$AAOI那样的批量订单增长阶段，股价将会超越60美元。
英文原文
It's not greed, it's fundamentals. $AEHR went up in price is because they landed the leader in optical transcivers. This could be $AVGO, $LITE, $COHR, $MRVL, $CSCO or others. Markets are forward looking. IMO it will blow away $60+ if it shifts into mass order ramp like $AAOI earnings.
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认为 AEHR 在小市值下被低估，正被多家超大客户做资格认证。
$AEHR 在大约 11 亿美元市值下看起来极有吸引力。 Aehr 开始让我想起早期的 $TER，又有点像财报前的 $AAOI。 如果把时间线和猜测客户列出来： 2 月 11 日：Sonoma 为超大客户的 AI ASIC 处理器拿到生产胜利。（大概率是 $GOOGL、$AMZN、$META） - 可能是谷歌？Aehr 收购了 Incal，而 Incal 曾被猜测会被谷歌用于 TPU。 2 月 26 日：来自 AI 主力客户的 1400 万美元。（大概率是 $AMD、$NVDA） - 这里更像是 $AMD，用于 Instinct MI300/MI400。 3 月 3 日：一台 FOX-XP 系统对应的主硅光客户。（大概率是 $INTC siph） - 很可能 $INTC 就是他们的主客户。 3 月 31 日：来自一家新的大型硅光客户的首单。（大概率是 $AVGO、$MRVL、$CSCO） - 新客户（排除了 Intel），大概是这些正转向 800G/1.6T 硅光收发器的公司之一。 （这些都属于非常保密的 BOM 级推测） 不管怎样，这条时间线对 $AEHR 来说就是在不断加码。 也许就是下一次财报。也可能再过两季。 但感觉量产订单只是时间问题。
英文原文
$AEHR looks extremely promising at ~$1.1B MC. Aehr is starting to remind me of an early $TER, mixed with pre-earnings $AAOI. If we look at the timeline and speculated customers: Feb 11th: Sonoma production win for Hyperscaler's AI ASIC processors. (likely $GOOGL, $AMZN, $META). - Probably Google? Aehr bought Incal, who was speculated to be used by Google for their TPUs. Feb 26th: $14 million from AI lead customer (likely $AMD, $NVDA) - Probably $AMD here for Instinct MI300/MI400. March 3rd: Lead silicon photonics customer for one FOX-XP system (likely $INTC siph) - Very likely $INTC has been their lead customer. March 31st: Initial order from major new silicon photonics customer (likely $AVGO, $MRVL, $CSCO ) - New customer (rules out Intel), prob one of these transitioning to 800G/1.6T silicon photonics transceivers (All speculative, very confidential BOM) Regardless. This timeline is just bottling up for $AEHR. Could be next earnings. Or two quarters from now. But feels like a matter of time before we see mass orders.
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展示CPO/光子学ETF模拟仓YTD表现，等权重投资收益率50%，看好光子学长周期。
Serenity的Silicon Photonics / CPO ETF模拟仓。各成分股年初至今收益率：$IQE：+282.5%，$AXTI：+246.6%，Landmark：167.54%，$AAOI：+157.37%，$SIVE：+113.08%，$SOI：+103.54%，$LITE：+100.27%，$LWLG：+92.35%，$VIAV：+88.71%，$AIXA：+73.92%，$AEHR：+70.4%，$CIEN：+67.67%，$FORM：+60.67%，$FOCI：+60.44%，$CAMT：+49.13%，$GLW：+46.77%，$SMHN：+45.94%，Fujikura：+43.89%，$COHR：+41.81%，$KEYS：+40.48%，$TSEM：+36.42%，$ASX：+29.89%，$MTSI：+28.34%，$NOK：+27.5%，Shin-Etsu：+27.33%，$ONTO：+26.28%，$BESI：+24.71%，$UMC：+18.11%，$INTC：+17.27%，$OXINF：+15.03%，$FN：+12.79%，Eoptolink：+11.82%，$TSM：+6.00%，$HIMX：+5.39%，$SMTC：+4.11%，Sumitomo：+3.67%，$CSCO：+3.25%，Innolight：+0.33%，$MRVL：+0.16%，$APH：-6.48%，$MXL：-7.62%，$AVGO：-7.99%，$POET：-12.99%，$TEL：-14.93%。这是回顾性的，但你们知道我在很多赢家仓位已经持有一段时间了（例如前六/七名，除了Landmark外的$AXTI或$LITE）。但如果你好奇年初投资整个光子学趋势的话，等权重收益率是多少？50.033%。我预计光子学长周期(supercycle)将持续未来数年，这些名字中的许多将成为未来的大受益者。特别是随着共封装光学(CPO)被用于扩展AI部署。光子学是AI的新架构范式。
英文原文
The Serenity Silicon Photonics / CPO ETF. YTD Returns of Each Index Stock: $IQE: +282.5% $AXTI: +246.6% Landmark: 167.54% $AAOI: +157.37% $SIVE: +113.08% $SOI: +103.54% $LITE: +100.27% $LWLG: +92.35% $VIAV: +88.71% $AIXA: +73.92% $AEHR: +70.4% $CIEN: +67.67% $FORM: +60.67% $FOCI: +60.44% $CAMT: +49.13% $GLW: +46.77% $SMHN: +45.94% Fujikura: +43.89% $COHR: +41.81% $KEYS: +40.48% $TSEM: +36.42% $ASX: +29.89% $MTSI: +28.34% $NOK: +27.5% Shin-Etsu: +27.33% $ONTO: +26.28% $BESI: +24.71% $UMC: +18.11% $INTC: +17.27% $OXINF: 15.03% $FN: +12.79% Eoptolink: +11.82% $TSM: +6.00% $HIMX: +5.39% $SMTC: +4.11% Sumitomo: +3.67% $CSCO: +3.25% Innolight: +.33% $MRVL: +.16% $APH: -6.48% $MXL: -7.62% $AVGO: -7.99% $POET: -12.99% $TEL: -14.93% This is retrospectively, but as you've known I've been in a lot of the winners for awhile (eg. Top 6/7 like $AXTI or $LITE aside from Landmark). However, if you were curious if you invested in the photonics trend as a whole at the start of the year. The equal weighted return? 50.033% I expect the Photonic Supercycle to last over the next several years, and many of these names to be large beneficaries going forward. Especially as CPO is used to scale AI deployments. Photonics is the new architectural paradigm for AI.
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发布CPO供应链全景图，预测明年CPO拐点，建议从EML瓶颈轮动至SiPh架构赢家。
即将发布的共封装光学(CPO)/硅光子学(Silicon Photonics)瓶颈速查表： $SIVE、Sumitomo、$LITE、$COHR、$AVGO、$MTSI、$AAOI - 光源（连续波分布式反馈激光器CW DFB Lasers） $TSEM、$GFS、$UMC、$TSM、$INTC - 硅光子学 foundry代工 $NOK、$CIEN、$CSCO、$COHR - DCO相干数字信号处理 $HIMX、FOCI (3363.TWO) - 微透镜+光纤阵列 $POET - 光学中介层(Optical Interposers) $SOI、$AXTI、Shin-Etsu - 衬底 $FN、$ASX、Innolight、Eoptolink - 光学封装与组装 $MTSI、$SMTC、$MRVL、$MXL - 模拟/混合信号集成电路 $LWLG - 投机性调制器材料 $GLW、$APH、$TEL、$FIT、Fujikura - 连接器和光纤 $FORM、$KEYS、$VIAV、$AEHR - 测试与测量 $BESI、$SMHN、$ONTO、$CAMT - 先进封装与混合键合(Hybrid Bonding) 其中许多是私营公司，如Lightmatter、Ayar、Ranovus等。 现在……每个人都在问……如何获利？ 如果你看CPO总可用市场(TAM)预测，它是一条直线上升，明年是CPO大规模部署的拐点。 阿尔法收益来自捕捉轮动： 从当前电吸收调制激光器(EML)瓶颈（$LITE、$COHR类型）到CPO的硅光子学/连续波分布式反馈激光器架构赢家。 最高上涨潜力的公司是那些不在当前周期内、但在下个周期内的公司。 $SOI、$SIVE或$AEHR就是完美例证。 搭乘当前可插拔(pluggable)瓶颈，如$AAOI。 但真正的阿尔法收益来自抢先机构布局下一波CPO瓶颈。 资本轮动不可避免。
英文原文
The upcoming CPO / Silicon Photonics Bottleneck Cheat Sheet: $SIVE, Sumitomo, $LITE, $COHR, $AVGO, $MTSI, $AAOI - Light Source (CW DFB Lasers) $TSEM, $GFS, $UMC, $TSM, $INTC - SiPh foundry $NOK, $CIEN, $CSCO, $COHR - DCO $HIMX, FOCI (3363.TWO) - Micro-lens + Fiber Arrays $POET - Optical Interposers $SOI, $AXTI, Shin-Etsu - Substrates $FN, $ASX, Innolight, Eoptolink - Optical Packaging and Assembly $MTSI, $SMTC, $MRVL, $MXL - Analog/Mixed-Signal ICs $LWLG - Speculative Modulator Materials. $GLW, $APH, $TEL, $FIT, Fujikura - Connectors and Fibers $FORM, $KEYS, $VIAV, $AEHR- Test & Measurement $BESI, $SMHN, $ONTO, $CAMT - Advanced Packaging & Hybrid Bonding Many are private companies from Lightmatter, Ayar, Ranovus and others. Now... Everyone is asking... How do you profit? If you look at the forecast for CPO TAM, it's a straight line up, and next year is inflection point for CPO mass deployment. The alpha is capturing the rotation: From the current EML bottlenecks ( $LITE, $COHR type) to SiPh / CW DFB architectural winners for CPO. Highest upside potential are the ones that aren't included in current cycles. But that are in the next. Companies like $SOI, $SIVE, or $AEHR are perfect examples. Ride the current pluggable bottleneck like $AAOI. But the alpha is frontrunning institutions with the next CPO bottleneck. The capital rotation is inevitable.
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认同思科因体量小而具投资潜力
@CapSimplified 不，我能理解为什么 $CSCO 是一个不错的候选标的。相比之下，它的体量相当小。
英文原文
@CapSimplified Nope, I can see why $CSCO is a good candidate. It’s a pretty small company in comparison.
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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!