The announcements reveal three distinct competitive plays, each with different assumptions about where AI adoption actually happens. OpenAI is doubling down on consumer lock-in through vertical integration: GPT-6 bundled with Intelligent UI rolling globally, College Planner targeting teens as early adopters, and hotel booking embedded directly into ChatGPT. This is the API-as-a-moat strategy executed at scale, making the interface itself the product rather than licensing capability to partners. Microsoft and NVIDIA are taking the opposite angle, positioning AI agents as native to Windows infrastructure through co-engineered hardware and software, which signals they believe the real value accrues to the operating system layer, not the chat interface. IBM and SAP are working backward from enterprise pain points: reducing hiring administrative work without system replacement, cutting operational cycle times by 50% through cloud ERP, and advancing quantum benchmarking toward practical fault tolerance. GitHub's focus on secret protection scaling with developer velocity suggests the market recognizes that tooling velocity has outpaced security practices, and whoever owns the developer workflow during the build phase captures leverage over what gets shipped. Hugging Face's move into edge decision models and fine-tuning for math competition performance indicates a play for the builder-to-builder market where cost and control matter more than brand. The pattern isn't labs racing toward AGI; it's competitors staking different claims on where the economic rent actually sits: consumer surface, infrastructure, enterprise operations, developer workflow, or edge inference.
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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