NVIDIA introduced the T3000 and T2000 Jetson modules based on the Thor architecture, advancing mainstream robotics and edge AI applications. These compact, power-efficient AI supercomputers enable mass-market deployment of general-purpose robots and autonomous machines. The new modules deliver high AI compute performance, integrated functional safety, and seamless running of the NVIDIA Halos for Robotics full-stack safety system.

While the vision of orbital data centers running AI promises unprecedented compute power, a recent IEEE Spectrum report, shared by Slashdot, critically assesses the immense technical and economic hurdles. Despite Elon Musk's ambitious predictions, the reality check underscores that space-based AI infrastructure remains far from practical, highlighting significant challenges in manufacturing, launch capacity, and especially, thermal management for powerful chips like NVIDIA's H100.

OpenAI co‑founder Greg Brockman testified that the company expects to spend $50 billion on compute this year, a figure tied to massive cloud and hardware deals. The revelation spotlights the economics of large‑scale AI and raises questions about profitability and investor expectations.