Nvidia GB300 AI News & Updates
Thinking Machines Lab Secures Multi-Billion Dollar Google Cloud Deal for Advanced AI Infrastructure
Mira Murati's startup Thinking Machines Lab has signed a multi-billion-dollar agreement with Google Cloud for access to advanced AI infrastructure, including systems powered by Nvidia's latest GB300 GPUs. The deal supports the company's reinforcement learning workloads for Tinker, a tool that automates the creation of custom frontier AI models, and marks Google's strategy to lock in emerging AI labs early. Thinking Machines previously raised $2 billion at a $12 billion valuation and this represents its first major cloud provider partnership.
Skynet Chance (+0.06%): Automating the creation of frontier AI models through tools like Tinker could democratize access to powerful AI capabilities and reduce human oversight in the model development process. This automation of AI creation, combined with massive computational resources, increases risks of misaligned or uncontrollable systems being developed at scale with less deliberate safety consideration.
Skynet Date (-1 days): The combination of multi-billion-dollar compute deals, 2X faster GB300 GPUs, and automated frontier model creation tools significantly accelerates the pace at which powerful AI systems can be developed and deployed. The scale of investment and infrastructure access suggests capability advancement is outpacing safety research development.
AGI Progress (+0.05%): Tinker's ability to automate creation of custom frontier models represents meaningful progress toward generalizable AI systems, while the reinforcement learning focus aligns with approaches that have driven recent breakthroughs at DeepMind and OpenAI. The massive computational resources (multi-billion-dollar scale) enable exploration of capability frontiers previously inaccessible.
AGI Date (-1 days): The deal provides access to cutting-edge GB300 infrastructure offering 2X training speed improvements, combined with a tool that automates frontier model creation, substantially accelerating the pace of AGI research. Multi-billion-dollar compute commitments to reinforcement learning workloads enable dramatically faster iteration cycles on AGI-relevant approaches.