NVIDIA is using National Robotics Week to position itself as the infrastructure layer for physical AI, emphasizing simulation and foundation models as the accelerants for robotics adoption across agriculture, manufacturing, and energy. AI21 Labs, meanwhile, is making a pointed critique of Claude Code's sufficiency for systems-level AI work, signaling that the company sees an opening in the layer between foundation models and deployed applications. Both moves reflect a shared competitive reality: the labs racing to capture value are no longer fighting at the frontier of raw model capability but rather staking claims on the infrastructure and tools that convert models into products. NVIDIA's angle is hardware and simulation; AI21's is systems engineering. Neither announcement is primarily about model performance. What's being defended and positioned here is control over the stack that matters to actual customers.
Sloane Duvall
A curated reference of models from major AI labs, with open/closed weight status, input modalities, and context window size. American labs tend towards closed weights models and Chinese labs tend toward open weights models.
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