$V
相关推文
按时间倒序
-
认为 Visa 和 Mastercard 不会消失，但未来增长会被免手续费支付网络和 USDC 大量蚕食。
@beauty_oe 我不认为 Visa 或 Mastercard 会消失。 但是，未来增长的大部分，可能会被免手续费的支付系统（支付轨道）以及 USDC 吃掉（被蚕食）。 我现在感觉，市场似乎在默认 $V（Visa）会永远持续复利增长。
英文原文
@beauty_oe VisaやMastercardが消滅するとは思っていません。しかし、今後の成長の大部分は、手数料無料の決済システム（決済レール）やUSDCに奪われる（カニバライズされる）可能性があります。 現在の市場は、$V（Visa株）が永遠に複利成長していくことを前提としている（織り込んでいる）ように思います。
-
AI代理支付以USDC为主且单笔金额极低，市场低估其对传统卡支付网络的冲击。
我对市场没有定价卡支付网络和交换费（如$V和$MA）的长期颠覆感到惊讶。 由$CRCL和$COIN推动。 来自Circle的全球市场负责人： "在过去九个月里，AI代理完成了1.4亿笔支付，总交易量达4300万美元。 其中98.6%以USDC结算，平均交易金额仅为0.31美元。" 像$PYPL这样的卡网络和百分比收费的支付处理器可能要被"烤熟"了？
英文原文
I’m surprised markets aren’t pricing in long term disruption of card networks + interchange like $V and $MA. By $CRCL and $COIN. From Global Markets Head at Circle: "Over the past nine months, AI agents completed 140 million payments with a total transaction volume of 43 million US dollars. Among these, 98.6% were settled in USDC, with an average transaction amount of only 0.31 US dollars." Card networks and % fee payment processors like $PYPL are likely going to be cooked?
-
分析多个软件股认为AI无法真正颠覆网络效应驱动的平台，存在投资机会。
"AI正在颠覆软件，但实际上并非如此"这个投资组合。以下是我个人偏好的清单：$RDDT（149美元，年初至今-37.47%）：10/10$NFLX（84.61美元，6个月-30.85%）：9/10$NET（169.5美元，年初至今-13.55%）：10/10$SPOT（488美元，年初至今-15.2%）：9/10$SNAP（5.13美元，年初至今-38.1%）：10/10$DUOL（85.3美元，年初至今-60.92%）：6/10$PINS（17.5美元，年初至今-34%）：8/10$U（18.83美元，年初至今-59.3%）：8/10$FIG（28.93美元，年初至今-23.65%）：7/10Reddit - 10/10：你可以用Opus一天内vibe code一个Reddit，但主要问题是"网络效应"是你无法复制的。你可以问ChatGPT或Gemini一个问题，但主要用途是英雄联盟赛后讨论或人类话语——这些才是人们使用Reddit的原因。Netflix - 9/10：你上Netflix看《鱿鱼游戏》和其他内容。AI确实会帮助生成新的病毒式电影或电视节目，但你仍然会用Netflix或YouTube来看。人们仍然会观看授权动漫如《孤注一掷》或最新的《弥留之国的爱丽丝》剧集，而不是AI生成的内容。Cloudflare 10/10：我不认为AI能颠覆Cloudflare。Spotify - 9/10：最大的颠覆是Apple Music不收取服务费（这可能不会发生）。虽然有AI生成音乐，但就像YouTube一样，你用Spotify来听它。你可以尝试vibe code Spotify，但很大一部分是版权。AI生成的音乐不会取代Martin Garrix的EDM配乐（其中很大一部分是知道这首歌→去音乐节现场）或Taylor Swift的《Love Story》类型歌曲（你也会去现场看演唱会）。Snapchat - 10/10：同样是网络效应——它之所以有效只是因为每个人都同意使用它。它的问题是变现和过多的股票薪酬，不是AI颠覆。Duolingo - 6/10：我长期以来一直嘲笑Duolingo，但在跌到86美元后估值再次合理。是的，你可以通过Gemini学习语言，我自己也是这么做的。但大多数人仍然会把Duolingo作为一种激励工具。还有品牌认知度+便利性，这是最大的因素。人们可以在应用商店vibe code自己的Duolingo，但这不意味着人们会使用它或自己出去编码。Pinterest - 8/10：这是人类相关的品味。人们担心代理商务和生成式AI（Midjourney、DALL-E）会颠覆这种搜索，但人们其实不太用Pinterest做这种搜索。Unity - 8/10：每个人都用Unity做4D AI和World Models。人们担心生成式游戏相关的事情，但它仍然被广泛使用。他们的主要问题是AI使用的变现，不是颠覆。Figma - 7/10：AI现在主要用于将你的Figma文件移植到html/css/js。并不完全是颠覆对软件的需求。也许只是我，但设计和创意有某种人类元素是AI还无法模仿的——用于线框图（它擅长复制和做相对新的东西）。我高度怀疑AI能否一键生成Tempo或$XYZ网站。对于$V和Mastercard等其他公司，我确实相信手续费和百分比费用可能会终结，因为AI通过$CRCL稳定币处理直接支付路由。对于$CHGG等其他公司，它们确实已经被颠覆出局了。对于$RDDT和$NFLX等其他公司，每个人仍然会去那里讨论或看《鱿鱼游戏》，AI没有实质性颠覆效应。由于恐惧已经笼罩软件市场，抄底将很难，但许多公司，如Reddit，提供了绝佳机会。
英文原文
The "AI is Disrupting Software but Not Really" Bucket. Here's my personal list of favorites: $RDDT ($149, -37.47% YTD): 10/10 $NFLX ($84.61, -30.85% 6M): 9/10 $NET ($169.5, -13.55% YTD): 10/10 $SPOT ($488, -15.2% YTD): 9/10 $SNAP ($5.13, -38.1% YTD): 10/10 $DUOL ($85.3, -60.92% YTD): 6/10 $PINS ($17.5, -34% YTD): 8/10 $U ($18.83, -59.3% YTD): 8/10 $FIG ($28.93, -23.65% YTD): 7/10 Reddit - 10/10: You can vibe code Reddit in a day with Opus. But the main thing is "Network Effect" that you can't replicate. You can ask ChatGPT or Gemini a question, but main thing is the type league of legends post-match discussions or human discourse that people use reddit for. Netflix - 9/10: You go to Netflix for squid games and others. I'm sure AI will help with generation of new viral movies or TV shows, but you will still use Netflix or Youtube to watch it. People are still going to watch licensed Anime like Solo Leveling or the newest Alice in Borderlands show over AI generated contnet. Cloudflare 10/10: I don't see how AI would disrupt Cloudflare. Spotify - 9/10: Biggest disruption is Apple Music just not charging for services (which likely won't happen). There's AI generating music but like Youtube, you use Spotify to listen to it. You can try and vibe code Spotify sure, but a large part of it is licenses. AI generated music will not go out and replace Martin Garrix EDM soundtracks (where a large part of it is knowing the song -> going to music festivals in person) or Taylor Swift Love Story type songs (where you also go in person to see concerts). Snapchat - 10/10: Same with network effect only reason it works is because everyone agrees to use it. Their issue is with monetization and excessive stock based compensation, not AI disruption. Duolingo - 6/10: I made fun of Duolingo for the longest time, but it's decent valuation again after the selloff to $86. Yes you can learn languages through Gemini, which I do myself. But most people are still going use Duolingo as more of a motivational tool. There's name recognition too + convenience, which is the biggest factor. People can vibe code their own Duolingo but that doesn't mean people will use it on the app store or go out and code it themselves. Pinterest - 8/10 - It's human related taste. People are fearing agentic commerce and generative AI (Midjourney, DALL-E) will disrupt this type of search, which people don't really use Pinterest for. Unity - 8/10: Everyone uses Unity for 4D AI and World Models. People fear stuff regarding generative gaming, but it's still widely used. Their main issue is monetization from AI usage, not disruption. Figma - 7/10: AI is right now is mainly for porting your figma files over to html/css/js. Not exactly for disrupting the need for the software. Maybe it's just me but there's a certain human element to design and creativity that AI can't emulate yet for wireframing (it's good at copying and doing something relatively new). I highly doubt AI can one-shot Tempo or $XYZ websites. For others like $V and Mastercard, I do genuinely believe interchange and % based fees maybe end as AI handles direct payment routing through $CRCL stablecoins. And for others like $CHGG, they have legitimately been disrupted out of existence already. For the rest like $RDDT and $NFLX, everyone will still go there for discussions or to watch Squid Games, and AI has no material disruptive effect. Going to be hard to time the bottom as fear has overtaken the market with software, but many, such as Reddit present a great opportunity.
-
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!