The infrastructure arms race is accelerating at a pace that dwarfs model announcements. NVIDIA and its Korean partners are committing to a compute buildout that will reach 200 megawatts initially, with NAVER alone targeting 1 gigawatt of deployment, a scale that reflects not experimental ambition but capital flowing toward the assumption that AI workloads will consume power at industrial magnitude. SK Group's $500 billion-plus partnership with NVIDIA across AI factories and memory supply is not a technology deal; it is a commitment to lock in supply chain positioning before the window closes. These are not press releases about capability. They are announcements of who will control the physical infrastructure that runs the models everyone else builds on. Meanwhile, AMD is releasing open-weight models, Instella-MoE and Poro 2 Long, trained on its own silicon and software stack. This is not generosity; it is a competitive move to prove ROCm viability and create a constituency of builders who have an incentive to support AMD hardware. Anthropic's Claude Opus 5 and red team pilot on drone control appear in the same news cycle, but the structural signal is elsewhere: the labs releasing models are increasingly peripheral to the labs building the factories. Compute capacity, not model weights, is the constraint that matters now, and the companies that understand this are making nine-figure commitments before the others catch up.
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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