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Starlink now reaches more people than live in China and India combined. • 3.3 billion addressable people across 164 countries • Bigger than the two largest nations on Earth put together Starlink now accounts for ~77% of all active maneuverable satellites in Earth orbit. • 12 million subscribers • 10,000+ satellites in orbit • Starlink Mobile already reaches over 7.4 million monthly unique devices across roughly 30 countries • 200+ Mbps during peak hours, with speeds exceeding 400 Mbps in many locations • Fiber-like internet performance from space • 650+ Direct-to-Cell satellites deployed, enabling unmodified smartphones to send text messages via satellite • V3 Starlink satellites with massive capacity increases coming soon About 33% of the global population doesn’t have internet access. Starlink will fix this.

Starship Was Engineered Backward From One Objective: Moving millions of tons to Mars at the lowest possible cost per ton to build a self-sustaining civilization. Five first-principles decisions make it possible: - Produce propellant on Mars. Methane and oxygen made from local CO2 and water ice via Sabatier. No Earth return fuel required. - Use 301 stainless steel. 67× cheaper than carbon fiber and survives both cryogenic propellants and 1,700°C reentry without heavy shielding. - Refuel in orbit. Tankers reset the rocket equation, enabling 100–150 ton payloads to Mars. - Belly-flop reentry. Maximize drag area so the atmosphere does most of the braking. - Full reusability. Both stages fly again rapidly. Marginal cost collapses to propellant only. This is the minimum architecture that makes multi-planetary life feasible.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

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CPO 쪽 추가 정리임. 이번 NVIDIA GTC Taipei 이후 CPO 투자 논리는 더 강해졌다고 봄. 가장 중요한 건 NVIDIA가 Spectrum-X Ethernet Photonics를 CPO 기반 스위치로 공식화했고, production 단계에 들어갔다고 표현했다는 점임. 이제 CPO는 “언젠가 될 기술”이 아니라, AI Factory 네트워크 병목을 풀기 위한 초기 생산·초기 채택 단계로 넘어가는 중임. 다만 보수적으로 봐야 할 부분도 있음. NVIDIA 제품 페이지 기준으로 Spectrum-X Ethernet Photonics의 상용 가용 시점은 2026년 하반기로 제시됨. 그래서 “now in production”을 곧바로 대규모 매출 인식으로 보면 과할 수 있음. 가장 합리적인 해석은 이거임. 공급망 생산 램프 시작 + 초기 고객 채택 확인 + 본격 상용 출하는 2026년 하반기. 기술적으로도 숫자가 꽤 구체적임. NVIDIA는 Spectrum-X Ethernet Photonics에서 최대 409.6Tb/s bandwidth를 제시했고, CPO가 기존 pluggable transceiver 대비 네트워크 전력 효율 5배, AI application runtime 5배, deployment time 1.3배 개선을 제공한다고 설명함. 이건 단순히 “광통신이 좋아진다”가 아니라, NVIDIA가 AI 데이터센터 네트워크 병목을 CPO로 풀겠다는 뜻임. 공식 생태계 명단도 중요함. NVIDIA CPO/Silicon Photonics 생태계에 언급된 회사는 대략 TSMC, Browave, Coherent, Corning, Fabrinet, Foxconn, Lumentum, SENKO, SPIL, Sumitomo Electric, TFC Communication 쪽임. 이름이 찍힌 회사와 안 찍힌 회사는 신뢰도를 다르게 봐야 함. 이 중에서 가장 강한 직접 증거는 Lumentum과 Coherent임. NVIDIA가 두 회사와 각각 대규모 전략적 파트너십을 맺었고, 광부품/레이저 공급망을 실제로 잠그는 구조로 보임. CPO에서 external laser source, 즉 ELS는 매우 중요함. 레이저를 스위치 내부 고열 환경에서 분리해 외부 모듈로 두면 열 안정성, 교체성, 수명, 유지보수성이 좋아짐. 그래서 LITE, COHR 같은 레이저·광부품 업체가 가장 직접적인 수혜축으로 보임. Corning, Browave, SENKO, TFC, Coherent는 fiber, connector, micro-optics, optical assembly 쪽에서 중요함. CPO는 광엔진만 있으면 끝나는 기술이 아님. Fiber attach, connector, polarization-maintaining fiber, micro-optics, detachable connector yield가 전부 병목임. 즉 CPO는 “광엔진 테마”가 아니라 광부품·패키징·테스트·조립 전체 병목 테마임. Foxconn과 Fabrinet은 CPO assembly/test와 switch chassis integration 쪽임. 특히 Foxconn은 CPO 스위치 출하가 올해 시작되고, 2026년 3분기 양산/출하 및 연간 1만 대 목표가 보도된 상태임. 이게 중요함. CPO가 연구실 데모에서 끝나는 게 아니라, ODM/EMS 양산 일정으로 내려오고 있다는 뜻임. SPIL은 고급 패키징/테스트 축임. CPO는 광부품만의 문제가 아니라, multi-chip module, wafer bumping, wafer sort, assembly, testing이 다 엮이는 첨단 패키징 문제임. 그래서 CPO가 커지면 ASE/SPIL 같은 패키징·테스트 쪽도 같이 봐야 함. 기술 구조상 가장 흥미로운 부분은 Spectrum-X Ethernet Photonics MCM package임. NVIDIA는 하나의 compact package 안에 32개 silicon photonics engine을 통합하는 구조를 제시함. 각 optical engine은 16 transmit + 16 receive lanes, engine당 3.2Tb/s 구조임. 이 말은 CPO 시대에는 단순 광트랜시버 조립보다 광엔진 수율, 자동화 조립, pre-screening, 테스트, thermal stability가 훨씬 중요해진다는 뜻임. ELS 쪽 숫자도 중요함. NVIDIA 구조에서는 기존 대비 레이저 수를 크게 줄이고, single-ASIC 버전에서 16개 ELS, quad-ASIC 버전에서 64개 ELS를 쓰는 구조로 설명됨. CPO 스위치가 늘어나면 ELS, 레이저, fiber attach, optical connector 수요가 같이 따라갈 가능성이 큼. 투자 관점에서 나눠보면 이렇다고 봄. 직접 수혜 근거가 강한 쪽 Lumentum, Coherent, Corning, Fabrinet, Foxconn, TSMC, Browave, TFC, SENKO, SPIL, Sumitomo Electric 간접 옵션성이 있는 쪽 Sivers, Ayar 관련 optical I/O/ELS 생태계, Jabil 연계 1.6T pluggable optics, O-Net/Enablence ELS 모듈 검증이 더 필요한 쪽 NVIDIA 공식 CPO 명단에 없는 소형 광학주, CPO 단어만 붙은 스토리주, 실제 고객·양산·수율·물량이 확인되지 않은 회사 특히 Sivers $SIVE는 구분해서 봐야 함. CPO/optical I/O/ELS 스토리는 흥미롭지만, 현재 확인 기준으로 NVIDIA Spectrum-X CPO 공식 생태계 명단에 직접 이름이 찍힌 회사는 아님. 대신 Jabil 1.6T LRO pluggable optical transceiver 협력, O-Net/Enablence ELS 협력, Ayar Labs optical I/O 관련 간접 노출이 있음. 그래서 SIVE는 “NVIDIA CPO 직접 밴더”라기보다 CPO/optical I/O/ELS 간접 옵션주로 보는 게 맞음. 이 차이를 흐리면 위험함. CPO 전체 thesis는 강해졌지만, 개별 종목 신뢰도는 완전히 다름. 공식 밴더와 인접 테마주를 같은 밸류에이션으로 보면 안 됨. 내가 앞으로 볼 체크포인트는 세 가지임. 첫째, Foxconn CPO switch 1만 대 목표가 실제 Q3 양산과 매출로 찍히는지. 둘째, Lumentum/Coherent의 NVIDIA 관련 수요가 실적 가이던스에 얼마나 빨리 반영되는지. 셋째, SIVE 같은 간접 후보는 Jabil/Ayar/O-Net 쪽에서 실제 qualification, purchase order, shipment, 2027 ramp 일정이 확인되는지. 결론적으로 CPO 방향성 자체는 이번 발표로 더 강해졌다고 봄. NVIDIA가 CPO를 단순 선택지가 아니라 million-GPU AI Factory용 네트워크 구조의 핵심 부품으로 밀고 있고, 생산·초기 고객·양산 일정까지 붙기 시작했음. 다만 포트폴리오에서는 반드시 구분해야 함. NVIDIA 공식 노출 / 양산 수혜 / 간접 옵션 / 단순 테마 이 네 가지를 분리해서 봐야 함. CPO thesis는 좋아졌지만, 모든 CPO 관련주가 같은 종목은 아님. 매수·매도 의견 아님. 개인 공부 기록.

🇺🇸🇨🇳 American engineers said only God could build a bridge here. China said hold my beer. The Shui Luo River Bridge now spans a 650-foot canyon, built using cantilever arms extended from both sides until they meet mid-air. It uses C80 steel-fiber reinforced concrete, nearly 3x stronger than typical materials. Quite impressive. Source: @rawbuildd YT0

一根碳纤维的环球旅行 Do you know how far a single carbon fiber can travel? From a wispy filament, to exquisite craftsmanship in the workshop, It transforms into golf clubs, pickleball paddles, trekking poles… #NewChapterOfJiangxi# #JiangxiOpportunity# #JiangxiFriends# #Xinyu#

Do you know how far a single carbon fiber can travel? From a wispy filament, to exquisite craftsmanship in the workshop, It transforms into golf clubs, pickleball paddles, trekking poles… #NewChapterOfJiangxi# #JiangxiOpportunity# #JiangxiCreates# #JiangxiFriends# #Xinyu#

Elon Musk built a second internet above the first one. Nobody asked him to. Thousands of satellites orbit at 550 kilometers. Moving at 25 times the speed of sound. Talking to each other through lasers in the vacuum of space. Musk: “Thousands of satellites providing low latency, high-speed internet throughout the world.” Before Starlink, satellite internet lived at 36,000 kilometers. Geostationary orbit. Signals traveling a tenth of the way to the moon before bouncing back. The lag made it barely functional. Musk dropped the altitude by 98%. One decision rewrote the physics of an entire industry. But the altitude wasn’t the real play. Musk: “There are laser links between the satellites. It forms a laser mesh. The satellites can communicate between each other and provide connectivity even if the cables are cut.” Every internet connection you’ve ever used runs through cables. Fiber optic lines buried in soil. Dragged across ocean floors. Threaded through chokepoints that every military maps before anything else. A single anchor drop can black out a country. An earthquake can sever a continent. The entire digital world hangs from threads in the mud. Musk built a network that doesn’t touch the ground. No cables. No trenches. No ocean floor. No single point of failure. A constellation of machines whispering to each other through light at the edge of the atmosphere. The men who tried before him weren’t fools. Gates backed Teledesic at the height of Microsoft’s power. Motorola built Iridium with the best engineers alive. Both paid someone else to reach orbit. Both went to zero. Musk owned the rocket. SpaceX made launch reusable. Built the satellites in-house. Flew them on its own rockets. Owned every inch of the chain from factory floor to orbit. That isn’t a cost advantage. It’s a moat no one can cross without first building a rocket company from scratch. Starlink passed 10 million subscribers as a side project. Every telecom executive on Earth watched it happen. Not one of them can explain the architecture underneath. They think he built a better satellite company. He built the only network that survives when the ground gives out. And the ground always gives out.

A simple software update now lets soldiers use the radios they already carry to stop enemy drones. L3Harris created this new tool called Wraith Shield, which works with their common Wraith radios like the AN/PRC-171 model that many U.S., NATO, and allied troops already have. The radios scan radio waves around them while an onboard computer detects signals from enemy FPV drones When a soldier spots one, they push a button, and up to 40 radios in the group work together to jam the drone’s signal, causing it to crash, hover in place, or return home. The update costs only a few thousand dollars per radio, and soldiers need no new gear or much extra training. It will soon support bigger groups and more radio types later this year. Older anti-drone tools can be expensive or require special teams, but Wraith Shield gives every small unit this capability using equipment they already have. It works best against radio-controlled drones and does not fully stop fiber-optic drones, fully self-flying ones, or specialized military drones. The first units are ready to ship now, and both the U.S. and other countries want them.