科技巨头们的"蜂巢思维"又发作了。 这一次，他们集体将目光投向了同一个目标：把数据中心建到太空去。 埃隆·马斯克在社交平台上的连番"轰炸"，率先点燃了这把火。但他并非孤军奋战，亚马逊和蓝色起源创始人杰夫·贝索斯、谷歌前CEO埃里克· 施密特早已是此理念的信徒，"芯片教父"黄仁勋也已高调入局。 2025年11月份，谷歌宣布将在2027年前发射原型卫星，测试其AI芯片的太空性能。上周，初创公司Starcloud甚至声称已在搭载英伟达芯片的卫 星上，训练出了首个太空大语言模型。 这一"太空狂想"固然令人心潮澎湃，但细究之下，其背后却可能隐藏着巨头们更为清醒甚至焦虑的认知：他们或许认为，地球已难以承载AI发 展所需的庞大计算力与能源。 更值得玩味的是，除了应对现实挑战，这一概念对于某些推动者还有着另一层意义。 这不只是技术畅想，也是一场资本叙事。 例如，有望在明年进行IPO的SpaceX，就急需一个能引爆市场的故事。在已有的火箭与星链叙事之 上，若再加上"太空AI基础设施"这个光环，无疑将使其估值获得巨大想象空间。 正如马斯克曾通过Optimus人形机器人等举措，对特斯拉所做 的那样。 然而，理想越宏大，质疑声往往也 ...

Investment Rating - The report maintains an "Outperform" rating for the communication industry [6] Core Insights - North American optical communication companies have shown strong quarterly performance, with significant growth in data center interconnect (DCI) demand [11] - The global computing infrastructure continues to benefit from high demand, particularly in AI and data center technologies [5][11] Summary by Sections Industry News Tracking - Fabrinet reported Q1 FY2026 revenue of $978.1 million, a 22% year-over-year increase and an 8% quarter-over-quarter increase, driven by strong DCI demand [11] - Coherent's Q1 FY2026 revenue was $1.581 billion, up 17% year-over-year, with a Q2 revenue guidance of $1.56 to $1.70 billion [15] - Lumentum's Q1 FY2026 revenue was $533.8 million, with a Q2 guidance of $630 to $670 million [17] - The launch of the scaleX640 super node by Inspur further enhances domestic computing power capabilities [19] - NVIDIA's StarCloud-1 satellite and Google's Project Suncatcher aim to establish AI computing infrastructure in space [25][29] Market Performance Review - The communication index rose by 0.92% this week, outperforming the CSI 300 index, which increased by 0.82% [50] - The optical devices/chips, operators, and satellite internet sectors performed relatively well [50][53] Investment Recommendations - Continuous focus on AI computing infrastructure development is advised, with recommendations for optical devices (e.g., Zhongji Xuchuang), communication equipment (e.g., ZTE), and liquid cooling technologies [60] - The three major telecom operators remain important assets for dividend allocation, with stable operations and increasing dividend ratios [60]

Core Insights - Nvidia has launched its H100 GPU into space, marking a significant milestone in space-based AI infrastructure, while Google has announced its own initiative, Project Suncatcher, to utilize solar energy for AI processing in space [1][3] - Project Suncatcher aims to create a scalable AI infrastructure using a constellation of satellites equipped with TPUs and free-space optical communication links, leveraging the vast energy from the sun [6][8] Project Overview - Project Suncatcher is designed to explore the potential of solar-powered satellite constellations to enhance machine learning capabilities in space, with the sun's energy being 1 trillion times greater than human electricity production [6][8] - Google plans to launch two prototype satellites in early 2027 in collaboration with Planet, addressing engineering challenges such as thermal management and system reliability in orbit [3][18] Technical Challenges - The system will require high-bandwidth, low-latency inter-satellite links to distribute machine learning workloads effectively, aiming for performance comparable to ground data centers [8][10] - Google has developed models to analyze the orbital dynamics of tightly clustered satellites, ensuring they can maintain stable orbits with minimal propulsion [10][12] TPU Radiation Resistance - Google's Trillium TPU has undergone radiation testing, demonstrating resilience to total ionizing dose and single-event effects, making it suitable for space applications [14][13] Economic Viability - Historical data suggests that satellite launch costs could drop below $200 per kilogram by the mid-2030s, making space-based data centers economically feasible [15][18] - The analysis indicates that the operational costs of space-based data centers could become comparable to terrestrial counterparts in terms of energy costs [15] Future Directions - The next milestone for Google involves executing a "learning mission" to test TPU hardware and models in space, paving the way for potential gigawatt-scale satellite constellations [18][19]

Core Insights - Google has launched Project Suncatcher, a space-based scalable AI infrastructure system designed to utilize solar energy for AI applications, with the potential to harness energy that exceeds human electricity production by 100 trillion times [8][11][29] - The project aims to deploy a constellation of satellites equipped with Tensor Processing Units (TPUs) and free-space optical communication links to enhance machine learning capabilities in space [7][9][10] Project Overview - Project Suncatcher is a significant exploration initiative that envisions a satellite constellation powered by solar energy, aimed at expanding the computational scale of machine learning in space [7][8] - The first satellite launch is scheduled for early 2027, in collaboration with Planet, to test the feasibility of the proposed system [3][29] Technical Challenges - The project faces several engineering challenges, including thermal management, high-bandwidth inter-satellite communication, and system reliability in orbit [28][29] - Achieving data center-scale inter-satellite links is crucial, requiring connections that support tens of terabits per second [13][14] - The satellites will operate in a dawn-dusk sun-synchronous low Earth orbit to maximize solar energy collection [13][21] TPU Radiation Tolerance - Google's Trillium TPU has undergone radiation testing, demonstrating resilience to total ionizing dose (TID) and single-event effects (SEEs), making it suitable for space applications [21][22] Economic Viability - Historical data suggests that launch costs for satellite systems may decrease to below $200 per kilogram by the mid-2030s, making space-based data centers economically feasible [23][24] - The operational costs of space-based data centers could become comparable to terrestrial counterparts in terms of energy costs [24] Future Directions - The initial analysis indicates that the core concept of space-based machine learning computing is not hindered by fundamental physics or insurmountable economic barriers [28] - The next milestone involves launching two prototype satellites to validate Google's models and TPU hardware in space [29][30]