Dear China shills: develop some standards

Nvidia at CES 2026: 4 Key Takeaways While the market remains focused on GPU ship counts, $NVDA is executing a broader "AI central nervous system" strategy. TL/DR: By open-sourcing the "brains" of the future—specifically the Alpamayo and GR00T models—Nvidia is ensuring its hardware becomes the indispensable infrastructure for the next decade of AI development. 1. The Rubin Era: $NVDA remains King Nvidia’s Rubin platform, the successor to Blackwell, is now in full production. Relative to Blackwell, Nvidia’s next gen Rubin architecture delivers: 4x increase in training performance = Faster time-to-market for frontier models. 10x reduction in inference costs per token = Allowing high-volume agentic AI to be profitable for enterprises. In a competitive data center market, Total Cost of Ownership (TCO) is the only metric that matters. By delivering 5x–10x gains annually, Nvidia makes it "economically irrational" to run older hardware, forcing a continuous upgrade cycle. 2. The Robotics Ecosystem $NVDA is moving beyond digital chatbots into Embodied AI—software that perceives, reasons, and acts in the physical world. Alpamayo: An open-source Vision-Language-Action (VLA) model that allows cars to reason through complex driving scenarios rather than just following rigid code. Cosmos AI: A foundation model for physical AI. It acts as a "world simulator," allowing robots to learn from synthetic data governed by real-world physics before they ever touch a factory floor. GR00T: A full-stack ecosystem providing the "brain" for humanoid and industrial robots. Jetson Thor: The hardware heart. A System-on-Module (SoM) based on Blackwell architecture, delivering the compute required for real-time robotic reasoning. 3. The “Google Android Ecosystem” Playbook Nvidia’s goal is to make GR00T the universal operating layer for robotics. By partnering with industry leaders like 1X, Agility Robotics, Boston Dynamics, etc., they are creating a massive network effect. Nvidia is using open-source software to win a standard-setting war: Open Weights, Proprietary Pipeline: While Alpamayo and GR00T weights are open, these models are architected to leverage the Rubin/Thor pipeline. Using a competitor’s hardware would require convincing a company to bypass the Rubin-to-Thor optimization path, which has been safety-hardened, necessitating a re-validation of the entire "chain of custody" for the AI’s logic to ensure no errors were introduced by the hardware switch. The Result: If a startup builds on the GR00T stack, they are effectively locked into Nvidia’s hardware for the entire lifecycle of the product. 4. Building the Moat Nvidia’s long-term vision is built on software friction and switching cost: Network Effect: As more developers contribute to the GR00T ecosystem, Nvidia’s proprietary libraries (e.g., CUDA-X and cuRobo) become the standard language of the industry, it becomes harder for a competitor like $AMD to convince a company to switch to a different, less-supported software stack. The CUDA Software: Nvidia has thousands of "fused kernels", these are pre-optimized software snippets built over years for every conceivable AI operation. For a competitor like the AMD MI400, even if it can demonstrate faster raw math, it lacks the decade of optimization (e.g., attention mechanisms) written specifically for Nvidia chips for all types of models/workloads. Switching Cost: The 2026 Mercedes-Benz CLA is the first vehicle to use the full Alpamayo stack. Because the CLA’s “Nvidia Drive" stack is already safety-certified (achieving top Euro NCAP ratings), switching to a rival chip would require months/years of re-testing and re-certification—a cost no major OEM is willing to bear. Three-Computer Architecture: Nvidia is telling customers they need three different computers for one robot: the Nvidia DGX/Rubin (to train it), the Nvidia Omniverse/Cosmos (to simulate it), and the Jetson Thor (to run it). This "Triple Lock" is what makes it so hard for a competitor to peel away a single customer—you don't just replace the chip; you have to replace the entire development workflow. The Goal: By giving away the "brains" of robots and cars, Nvidia is lowering the R&D barrier for companies like Mercedes-Benz and others, while also ensuring that their high-margin hardware becomes the industry’s central nervous system. (Not Financial Advice)

SK Hynix warns DRAM supply growth will lag demand through 2028. wccftech.com/sk-hynix-warns-…

Five years in Night City. Endless memories. One legendary GPU. To celebrate the anniversary, we’re giving away a custom Cyberpunk 2077 GeForce RTX 2080 Ti signed by Jensen Huang. Comment "GeForce Season" to enter 🌆

Are you posting ur coping lifestyle?