The infrastructure layer is consolidating around NVIDIA's hardware and financing apparatus while application-layer competition fragments across open and closed models. NVIDIA announced $500 billion in third-party capital mobilization through partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR, effectively converting compute into an investable asset class and locking in demand across the entire stack. Simultaneously, the company is addressing the physical constraint that has become the actual bottleneck: power delivery architecture to GPUs, not raw wattage. This signals that the constraint on scaling is no longer chip design but the unglamorous infrastructure problem of getting electricity to the hardware. Meanwhile, OpenAI and AWS are bundling Daybreak cybersecurity models through Amazon Bedrock, Google is advancing AMIE toward clinical-grade audio-visual consultations, and IBM has signed a multi-year deal with Together AI to scale open-source inference on NVIDIA hardware via IBM Cloud. Mistral is positioning around European data residency and sovereign AI, Hugging Face is optimizing token efficiency for smaller models, and NVIDIA itself is releasing Nemotron 3.5 Lightning for local agentic AI workloads. The pattern is clear: NVIDIA controls the capital formation and hardware stack; everyone else is competing on where models run, who owns the data, and which vertical applications justify the infrastructure spend. The open-source layer is no longer a fringe concern but a legitimate deployment path, which explains why NVIDIA is actively promoting it rather than treating it as cannibalistic. The real competitive moat isn't the model anymore. It's infrastructure financing, power architecture, and regional control.
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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