NVIDIA is moving "up the stack" from chips to an AI agent software platform to diversify its business and create a new moat beyond its CUDA system. By courting enterprise partners, NVIDIA aims to maintain infrastructure dominance even if AI labs succeed with their own custom silicon, reducing reliance on NVIDIA GPUs.

The unified support for 'open-weight' models from rivals like NVIDIA, Google, and Microsoft is a calculated move to prevent regulatory capture by market leaders OpenAI and Anthropic. By framing open access as vital for competition and innovation, this coalition aims to block potential government bans that would cement the dominance of a few closed-model labs.

NVIDIA possesses a powerful strategic weapon: the ability to release a frontier-level open-source model. This could undermine the business case for customers developing their own custom ASICs by commoditizing the model layer, thus reinforcing NVIDIA's dominance in the hardware ecosystem.

Reflection AI shifted its entire strategy overnight to building foundational open-source models after a meeting between its founder and Nvidia's CEO. This move serves Nvidia's strategic interest in diversifying the AI ecosystem and chip demand away from giants like OpenAI who develop proprietary silicon.

The "CUDA moat" is misunderstood. NVIDIA's true advantage is that major open-source models (e.g., from DeepSeek, Alibaba) are co-designed for its GPUs. This creates a powerful downstream effect where developers must use NVIDIA hardware to run the best available models, regardless of the programming layer.

The letter signed by Meta and NVIDIA isn't just about innovation; it's a strategic move to prevent closed-source leaders like OpenAI from cornering the market. Signatories have a vested economic interest in ensuring an open-weight ecosystem thrives, preventing all customer revenue from flowing to proprietary models.

Nvidia maintains partnerships with everyone, including rivals. By positioning itself as a neutral, essential supplier rather than a direct competitor, it has become central to every company's AI bet, securing its dominant and indispensable market position.

Nvidia is heavily investing in its own open-source models like Nemo Tron. This strategy ensures that as the open-source ecosystem grows, demand for its hardware also grows, positioning Nvidia's chips as the default platform and reducing reliance on closed-source model providers who act as intermediaries.

Jensen Huang strategically allocates GPUs to NeoClouds and new AI labs to prevent a world dominated by a few hyperscalers building their own custom chips (like TPUs). This ensures a diverse customer base and prevents NVIDIA's core products from being commoditized by a handful of powerful buyers.