
MtRainier
629 posts








THE OPTICAL PHOTONICS BOTTLENECK As AI clusters scale past copper’s physical limits, the bottleneck shifts to optical & these are the companies building that layer across the stack: 1. $AAOI building the transceiver layer of the AI network through vertically integrated U.S.-based InP laser manufacturing. It has already secured over $200M in its first volume 1.6T order from one hyperscale customer followed by another $124M in 800G orders from a second. 2. $AEHR building the reliability layer for the optical & AI hardware stack through burn-in & test systems. It just received a record $41M follow-on order from its lead hyperscale customer reinforcing the idea that Sonoma is becoming a key production burn-in platform for high-power AI ASICs. 3. $CRDO building the connectivity layer that helps AI clusters move data faster through active electrical cables, retimers & high-speed interconnect silicon. The DustPhotonics acquisition also extends that platform into silicon photonics before copper becomes a real constraint. 4. $LITE building the laser layer of the AI optical stack through EMLs, optical components & optical switching exposure. The setup is backed by a $2B $NVDA strategic investment & optical circuit switch backlog above $400M with orders reportedly extending through 2028. 5. $VIAV building the testing & validation layer of the optical stack through network instrumentation & photonics measurement tools. It is the picks-and-shovels layer of the transition because every high-speed optical buildout still needs to be tested regardless of which transceiver vendor wins. 6. $COHR building one of the core photonics bottlenecks through indium phosphide lasers, optical engines & communications components tied to next-gen AI networking. It also has a $2B $NVDA strategic investment behind it & is doubling InP device capacity into the 1.6T ramp. 7. $MRVL building the DSP & optical infrastructure layer through electro-optics, PAM DSPs, interconnect silicon & custom networking chips. The Celestial AI deal & NVLink Fusion exposure both strengthen its position as photonics becomes more central to AI cluster design.





GPT2 x 建筑图鉴 x 提示词 知识性拉满,而且它会联网喝茶信息准确定 然后仔细作图,真用心呀 顺着图片看, 我学到了不少信息 肝了3个小时, 还是没有达到我要求的手绘感觉 但是刊载知识性强烈的面上, 果断发布 你可以最后自定义尺寸和建筑名,让它提供图解。 💬Prompt 见评论区一楼









我最近也是类似的想法 VR300配备220TB LPDDR5,一年销量大概是5.5EB(甚至没算HBM) 手机市场LPDDR平均内存大概6~7GB,到2027年手机市场按30%+衰退计算(1.2->0.8B销量),有概率还不到5.5EB Nvidia一个公司单个产品的LPDDR用量,超过了全人类手机消费电子的用量,而且以后GPU的HBM消耗是指数型上涨 人类内存消费增长不大,十年DDR单机装机容量才三倍(7.xGB->23GB),而ai内存消费每一代架构都在翻倍 也就是说ai对内存的消耗是人类的倍数越来越大,指数型拉开差距 唯一限制这个差距拉大的,只有产能扩充的速度

卧槽!知名记者指出苹果CEO库克的管理盲区,过度依赖表格数据将导致无法应对地缘风险。 在最近的一段访谈中,记者Patrick McGee透露了库克被称为表格先生的原因。他分享了一个轶事,库克第一次接手原本只需两小时的每周数据复盘会时,竟然开了13个小时。他对细节有着非常执着的要求,并且把这种工作方式要求到了所有下属身上。 他提到,1998年库克刚加入苹果时,团队里戴眼镜的人并不多。但没过几年,大家几乎都戴上了眼镜。因为他们每天都要盯着特大号的纸张,核对无数个Excel表格里的供需数据。一部手机里有上千个零部件,他们必须检查所有细节来掌控全球供应链。 随后McGee提出了他的担忧。他认为库克确实完美践行了能被测量的才能被管理这句名言,这也是苹果能拥有超高利润率的核心所在。 但问题是,那些无法被写进Excel表格里的隐患,比如给竞争对手国家提供供应链支持所带来的地缘风险,到底该填在表格的哪一列。他总结说,这种对于量化数据的绝对依赖,恰恰是库克现在最大的盲点。


@aleabitoreddit Did you look into superconductor companies like $asmc yet?










