NVIDIA was a first-wave signatory of the open-model letter to prevent market consolidation. A future with only one or two dominant AI labs would create a customer monopsony, giving those labs immense pricing power over NVIDIA. A broader, more competitive AI ecosystem is in NVIDIA's best interest.

With its acquisition of Hugging Face, NVIDIA has become the most important bulwark against a closed-source AI oligopoly. By controlling the hardware layer and the leading open-source platform, Jensen Huang can offer a vertically integrated, sovereign alternative to companies dependent on OpenAI and Anthropic.

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.

Even pro-open-source executives may shift their stance if a closed-source model guarantees a profitable oligopoly. The key is ensuring enough profit distribution among major players (like Nvidia, Google, Anthropic), making them collectively prefer a controlled, lucrative ecosystem over the amorphous, less-monetizable open-source world.

Large AI companies advocate for regulation not out of genuine concern, but to create 'regulatory capture.' The high compliance costs become a moat that protects them from smaller startups and free open-source alternatives, effectively creating a government-sanctioned oligopoly.

NVIDIA's vendor financing isn't a sign of bubble dynamics but a calculated strategy to build a controlled ecosystem, similar to Standard Oil. By funding partners who use its chips, NVIDIA prevents them from becoming competitors and counters the full-stack ambitions of rivals like Google, ensuring its central role in the AI supply chain.

The policy debate over open-weight AI models is influenced by the commercial interests of large labs with closed, proprietary models. These labs view open-source alternatives, from the US or China, as direct competitors and are likely to be more skeptical of them in policy discussions.

Arguments against open-source AI from large labs are not based on safety but are a thinly veiled attempt to eliminate competition. These companies, which built their success on open academic research, now seek to use regulation to create a moat against the open-source community they once benefited from.

The push for AI regulation, often led by companies like Anthropic, is likely leading toward an attempt to ban open-source models. The justification will be that open models lack guardrails and are therefore dangerous, effectively cementing the power of a few closed-source providers.