The announcements today reveal a market consolidating around operational deployment rather than model capability claims. OpenAI is pushing Codex into production workflows, mobile access, real-time steering, deployment at Sea Limited, while AWS moves beyond model selection into prompt optimization tooling that lets customers compare outputs across five models simultaneously. This is the infrastructure layer asserting itself. Anthropic is signing enterprise deals with PwC and the Gates Foundation, positioning Claude as the implementation engine for existing organizations rather than the research frontier. Microsoft's dairy cooperative story and IBM's consulting pivot both signal the same thing: the money is moving from "we built a model" to "we'll run your operation with it." Meanwhile, the infrastructure plays, AMD optimizing inference on Kimi-K2.5, Hugging Face publishing embedding improvements and batching efficiency gains, NVIDIA shipping games on GeForce NOW, are all competing for the same outcome: making deployment cheaper and faster than the previous generation. None of this is about capability breakthroughs. It's about who owns the layer between the model and the customer's actual problem. The labs that win will be the ones that make that layer invisible.
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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