During the 2026 World AI Conference on July 18, Guoxing Aerospace and SenseTime announced a strategic partnership to develop the SenseTime Computing Constellation. This collaboration marks a pivotal shift in artificial intelligence infrastructure, transitioning space computing from isolated engineering experiments into a systematic component of the global AI supply chain. Under the announced roadmap, the initiative will progress through single-satellite validation, platform construction, and eventually constellation networking, targeting a scale of over one thousand satellites and ten thousand petaflops of computing capacity. The partnership fundamentally redefines the relationship between orbital assets and large-scale AI development. SenseTime has become the first large model enterprise to systematically access Guoxing's orbital computing architecture, enabling direct integration between mature terrestrial AI platforms and space-based resources. Rather than merely deploying isolated computing payloads, the companies aim to establish a space-Earth hybrid cloud. This framework will dynamically allocate tasks based on latency requirements, data location, and computational scale, leaving real-time orbital processing to satellites while reserving massive training workloads for ground-based data centers. This development reflects a broader industry evolution toward orbital data centers. While early space computing focused on demonstrating in-orbit processing capabilities, the current paradigm emphasizes infrastructure reliability, standardized interfaces, and continuous commercial service delivery. Similar initiatives by global technology firms confirm a convergence on leveraging low Earth orbit as a dedicated computational layer. The primary technical challenge now lies in orchestrating thousands of nodes across a dynamic orbital environment, ensuring stable inter-satellite communication, robust power management, and fault-tolerant scheduling. Commercial sustainability will depend on identifying scalable use cases that capitalize on the orbital advantage. Space computing power is best suited for high-volume in-orbit data preprocessing to alleviate downlink congestion, as well as providing low-latency inference for remote or terrestrial network-restricted devices. These applications position orbital infrastructure as a complement to, rather than a replacement for, ground-based supercomputing clusters. Overcoming high launch costs, radiation hardening, and thermal management remains critical for achieving cost-effective scaling. The Guoxing-SenseTime alliance signals that space computing is transitioning from experimental aerospace technology to an operational utility within the AI ecosystem. As orbital resource coordination matures, measuring the sector's success will shift from verifying isolated in-orbit computations to evaluating sustained, networked service delivery. The successful integration of space-based compute into mainstream AI development pipelines could establish a new architectural standard, where orbital and terrestrial data centers function as a unified, globally distributed processing network.