OpenAI is running a full-court press on production adoption, flooding the zone with use-case documentation that treats Codex and GPT-5.5 as solved infrastructure rather than experimental tools. Finance teams building models. NVIDIA engineers shipping systems. AutoScout24 scaling development cycles. Parameter Golf, which drew over 1,000 participants, reframed AI-assisted research as a constraint-optimization problem rather than a frontier mystery, signaling that the company sees the research community as a deployment lever, not an audience for papers. Meanwhile, the infrastructure layer is consolidating around specialized, cost-optimized hardware: AWS Redshift's Graviton-based instances cut price per compute unit while integrating open table formats, directly targeting enterprises already locked into data warehouse workflows. NVIDIA and SAP are bundling governance controls into agent platforms, recognizing that enterprises will not adopt without compliance machinery built in. IBM is packaging Red Hat services as managed inference and virtualization on its cloud, positioning itself as the safe, governed alternative for organizations that treat open source as a compliance requirement rather than a feature. GitHub's Copilot CLI, meanwhile, is still in the narrative-building phase, showing off what's possible rather than announcing adoption at scale. The pattern is clear: the labs have stopped arguing about capability and moved entirely to distribution, cost, and trust. OpenAI owns the narrative on capability. Everyone else is fighting for the enterprise install base by solving the problems that actually prevent purchasing decisions: price, governance, integration, and the ability to point to a managed service rather than a build-it-yourself platform.
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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