NVIDIA's Jensen Huang has just penned his first post on X, advocating for open-weight AI models as the Trump administration mulls steps to thwart China's AI labs from leapfrogging by distilling US frontier models.
Huang's advocacy is somewhat self-serving, though, especially as open-weight AI models are only expected to increase the overall demand for compute, including GPUs, while NVIDIA has done all it can over the past few years to maintain and extend its CUDA moat.
NVIDIA's Jensen Huang used his inaugural post on X to advocate for open-weight AI models
Jensen Huang has just posted a letter that has been signed by NVIDIA, Microsoft, Meta, Dell, Perplexity, Palantir, Mistral AI, and more, advocating for the proliferation of open-weight AI models.
Don't get me wrong. This initiative's moral compass points straight north. After all, OpenAI and Anthropic can't be allowed to become the ultimate arbiters of a technology as revolutionary as AI. Also, an open-weight AI economy stands a better chance of furthering healthy competition while reducing data security- and cost-related barriers, all the while aligning with the ethos of American democracy.
Of course, this issue came to a head only recently, when Anthropic and some members of the Trump administration accused Moonshot of distilling its Kimi K3 model from Anthropic's Fable. However, as has been repeatedly pointed out, Fable was unbanned towards the end of June while Moonshot released its Kimi K3 model around the middle of July. There just wasn't enough time for Moonshot to have distilled a model as massive as Kimi K3, not to mention its unique architectural feats that aren't a part of Fable. Moonshot likely fine-tuned some responses of its model using Fable though.
And, it's not as if OpenAI and Anthropic are innocent victims here. Weren't their older models trained on data from the internet, including copyrighted one?
This brings us to the core matter. Open-weight models do increase the demand for compute as distributed and fragmented deployment across every enterprise then becomes the norm, with each such deployment entailing dedicated compute and memory requirements, especially where data security concerns take precedence. In contrast, closed-weight models concentrate compute load within data centers controlled by neoclouds and hyperscalers.
Therefore, it's quite self-serving of Huang to now call for an open-weight AI economy, especially as NVIDIA has done all it can over the past few years to ensure that its CUDA moat remains resilient, from providing free GPUs and training to university and researchers, to coupling NVIDIA Collective Communications Library (NCCL) - a handy tool/library for connecting multiple GPUs - with proprietary hardware like NVLink and InfiniBand.
Perhaps, the open-weight AI model advocacy movement needs better salesmen to hammer home the primary point, preferably those who don't have a vested financial interest in its success.
About the author: Writing is my one incontrovertible passion. Over the past six years, he has authored over 2,200 distinct articles on financial and tech-related topics, spanning nearly 1 million words. And he has been a member of Wcctech mobile team since 2025. As an alumnus of the University of Toronto, Rotman Commerce Program, I bring nuance, in-depth knowledge, and a unique perspective to every topic that I cover. When I'm not writing, I'm traveling the world, exploring hidden confectionaries and restaurants as an aspiring food connoisseur.
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