SpaceX is rapidly converting its AI ambitions into a major data-center buildout. The analysis says the company has raised its 2027 capacity target to roughly 13 GW, with 3.6 GW already under construction and more than 9 GW of sites identified. Its case rests on exceptionally fast construction: SpaceX’s Colossus projects have reportedly brought facilities online in 91 to 122 days, while a new Mississippi site is expected to take about four months. The expansion also extends beyond existing AI campuses. Starbase, traditionally a rocket-development and launch site, is being fitted for two data centers that could together exceed 1 GW. The planned Texas TeraFab chip complex may include data-center capacity as well, potentially available to outside customers.
The buildout’s speed is attributed to SpaceX’s engineering and supply-chain capabilities, including Tesla-made modular infrastructure, alongside a willingness to choose power sources as construction advances. Analysts identify more than 8 GW of likely behind-the-meter power, including gas turbines, and describe a large battery-storage installation at Colossus 2: nearly 5.9 GWh is being deployed. That site is also seeking a grid connection, but projected generation and demand may still leave a power shortfall. The analysis argues batteries, efficient cooling, varied workloads, and different reliability requirements could help bridge it. Overall, it presents SpaceX as a potential world-leading AI cloud operator, while emphasizing that power availability, permitting, and the final use of sites such as TeraFab remain consequential constraints.
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Thinking Machines introduced Inkling 2 Small, a 594-billion-parameter mixture-of-experts model with image input and stronger coding, vision, reasoning, and autonomous-work capabilities. It is available through Tinker with closed weights, with Apache 2.0 weights planned after safety testing. (thinkingmachines.ai)
China plans comprehensive AI rules covering model development, data, computing, and deployment, with safety standards, ethical guidance, and regular reviews. It will ease compliance through transition periods and regulatory sandboxes while requiring companies to disclose risks and incidents and tracking AI’s effects on jobs. (bloomberg.com)
AI can boost productivity as people progress from autocomplete to coding assistants and agents, but overreliance can send them past a peak into low-quality “slop.” The author explains who is most at risk and how to avoid or recover from the habit. (thomasjiralerspong.substack.com)
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Z.ai’s upcoming GLM-5.5 models, in 32B, 122B, and 256B sizes, use an internal training pipeline spanning web pages, research papers, books, code, video, and audio. The pipeline includes 1 trillion web pages, 2 billion scientific PDFs, and 500 million code repositories. (twitter.com)
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