The announcements today reveal two distinct competitive theaters. One cluster centers on infrastructure and manufacturing, NVIDIA and AMD racing through MLPerf benchmarks, NVIDIA expanding partnerships with HPE and Coherent to lock in the hardware stack, AMD pushing distributed inference optimization on its Instinct line. This is competition over who owns the training and serving layer, where wins compound. The other cluster scatters across applications: OpenAI betting on pre-deployment simulation to reduce safety friction in releases, Google pairing climate work with housing planning to signal regulatory alignment, Microsoft and NVIDIA pushing agents into field devices and enterprise workflows. What's notable is the absence of a unified narrative. These aren't coordinated moves toward a shared vision of AI's next phase, they're parallel plays on different bets. OpenAI is optimizing for faster iteration and reduced friction at release. Google is optimizing for government partnership and public goods framing. NVIDIA is optimizing for supply chain control and platform lock-in. AMD is optimizing for competitive parity in training benchmarks and inference efficiency. IBM is optimizing for enterprise anxiety about data sovereignty. The infrastructure race has clear winners and losers. The application race has room for many players, which is why you see Microsoft in Turkish farming, NVIDIA in AR glasses, Anthropic in coding agents, and Google in housing. The money is moving fastest where switching costs are highest, that's the hardware layer. Everything else is still fighting for distribution.
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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