OpenAI is consolidating its position as the primary vendor of production AI agents by shipping GPT-5.5 directly into Codex, its application layer for knowledge work automation, while simultaneously ensuring that layer runs on NVIDIA's infrastructure. The move is revealing: rather than compete on model weights alone, OpenAI is bundling model capability with workflow orchestration, automations, plugins, skills, and structured task execution, which creates friction for customers to migrate. NVIDIA's public embrace of Codex running on GB200 systems signals that the infrastructure vendor sees agent frameworks as the real margin driver, not just raw compute. Meanwhile, Google DeepMind's work on distributed training resilience and Anthropic's partnership with NEC to build engineering capacity in Japan suggest the field is past the phase of proving model capability and into the phase of scaling production deployment and workforce alignment. Hugging Face's focus on browser-based transformer inference via Chrome extensions points toward a different vector: moving model execution to the edge and away from centralized inference, which could fragment the cloud-based agent stack that OpenAI and NVIDIA are building. The announcements collectively reveal a market sorting into layers, model vendors securing inference infrastructure partnerships, application vendors building stickiness through workflow automation, and infrastructure players ensuring they own the hardware dependency, with competition happening at integration points, not at the model level alone.
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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