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AI Preventing Space Collisions: 2026 AI-driven Space Traffic Management (STM) and Situational Awareness (SSA) Trends As of 2026, humanity is facing a busier and more congested outer space than ever before. Driven by the surge of low Earth orbit (LEO) mega-constellations led by SpaceX's Starlink and Amazon's Project Kuiper, there are tens of thousands of active satellites orbiting Earth. Consequently, the risk of orbital conjunctions and collisions has increased exponentially. Collisions between satellites, or between a satellite and space debris, represent a catastrophic threat. Beyond the loss of multi-million dollar equipment, they risk triggering the Kessler Syndrome —a domino-effect chain reaction of collisions that could render orbits completely unusable for generations. To prevent such a space catastrophe, the space industry in 2026 is actively deploying Artificial Intelligence (AI) to revolutionize Space Situational Awareness (SSA) and Space Traffic Management (STM) ...

AI Heading to Space: The Convergence of AI and Space Tech Revealed by Google Trends Analyzing recent search trends on Google Trends reveals a significant intersection capturing the attention of both the public and the global IT industry: the convergence of Artificial Intelligence (AI) and Space Technology (Space Tech) . Especially in June 2026, the massive computing deal between Google and SpaceX, alongside the upcoming SpaceX IPO scheduled for June 12, have emerged as dominant keywords in global search queries. In this article, we dissect the rapidly growing integration of AI and Space Technology based on recent Google Trends data and share actionable insights for developers and technology business leaders. 1. The $920 Million Monthly Google-SpaceX Compute Deal: The Dawn of Orbital AI A major spike in global search trends recently occurred following the news of a $920 million monthly cloud computing deal between Google and SpaceX . The catalyst behind this monumental agreement...

The Real Bottleneck of the AI Era: Power Grids, Not GPUs Centering on South Korea's 260k GPU Plan and Japan's Power Awareness Differences When discussing AI infrastructure competition, we usually look at the number of GPUs first. News focuses on how many NVIDIA GPUs a country has secured, how much Blackwell a company is adopting, and the scale of AI supercomputers being built in EFLOPS. However, recent situations show that the real bottleneck might not be the GPUs themselves. Even if you secure GPUs, you cannot run any of them unless you have data centers to plug them into, power to supply to those data centers, and a transmission grid to send that power to the actual locations. The announcement that South Korea has decided to secure GPU infrastructure on a scale of over 260,000 units from NVIDIA clearly illustrates this issue. According to NVIDIA's official announcement, the Korean government, Samsung Electronics, SK Group, Hyundai Motor Group, and NAVER Cloud will co...

AWS RNG vs NVIDIA CPO: A Comparative Analysis of Next-Gen AI Data Center Networks The recent announcements of AWS's Resilient Network Graphs (RNG) and NVIDIA's Co-Packaged Optics (CPO) represent two landmark innovations designed to power next-generation, large-scale AI data centers. While both technologies aim to solve the critical "network bottleneck" of AI clusters, they operate at entirely different layers of the networking stack. In many industry articles, RNG and CPO are framed as competitors, but from an engineering perspective, they are actually highly complementary. In this article, we will analyze and compare both technologies through the eyes of a network architect, DBRE, SRE, and infrastructure engineer. Why Networking Has Become the Bottleneck in the AI Era In traditional enterprise or web architectures, computing (CPU) or storage was usually the bottleneck. In Large Language Model (LLM) training environments, the paradigm has completely shifted. ...

Changing the database Compatibility Level in SQL Server is far more than just a matter of "version support." It is a major architectural decision that determines how queries are optimized, how cardinality is estimated, and whether modern Intelligent Query Processing (IQP) features are utilized. When a specific query slows down, database administrators often temporarily restore performance by lowering the compatibility level. However, this comes at a steep price: sacrificing the optimizer improvements of the latest database engine for the entire database. In this post, we will look at the differences between Compatibility Level 100 and 160 from a Query Store perspective and share a practical database tuning strategy. 1. Does Query Store Work Under Compatibility Level 100? One of the most common misconceptions is that "Query Store cannot be used under legacy compatibility levels like 100." Simply put, this is false. Even if the Compatibility Level is set to 100...