Why Open Models Decide Who Wins AI

Jensen Huang, CEO of NVIDIA, says, for my first post, I'm sharing a letter NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. Go in here and, and read a few excerpts, but you could see here, open weights and American AI leadership. I'll just read this. In the 1980s, early open-source software pioneers challenged the prevailing belief that software would advance only if companies kept tight control over their code. This movement pushed for a transparent ecosystem where developers around the world could study, modify, and improve software. Software developed by the open-source community now supports most of the internet and underlies systems used by the world's largest technology companies, as well as the US military and federal agencies conducting scientific research, cybersecurity, and other critical missions. Open source did more than lower the cost of software. It created a shared foundation of knowledge on which generations of American engineers and entrepreneurs built their institutional sovereignty. The United States now faces a similar choice with artificial intelligence. Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong open ecosystem that diffuses into every sector. This is essential for creating opportunities for innovation and prosperity across the country. It requires expanding access to AI, encouraging competition, robust application layers, and giving Americans greater control over the technology they rely on. Open weight models, AI models that anyone can download, inspect, modify, and run on their own infrastructure, are an important part of that foundation because they make advanced AI more accessible, adaptable, and widely available. Open weights expand access to the AI economy. Startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontier model prices for every task. Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient specialized models everywhere else. That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks. America wins the AI era by diffusing it into the workflows of factories, hospitals, farms, classrooms, and Main Street businesses. Open weights also strengthen competition, and competition is what keeps the gains of AI broadly shared rather than concentrated in a few open hands. By allowing many organizations to build, adapt, and deploy advanced models, Open weights create rivalry not only among model developers, but across cloud chips, applications, and services. That competition spurs innovation, drives down costs, and distributes the benefits of AI broadly across our economy. Open weights also give customers greater control. As organizations invest in AI, they want to know that they will not become locked into a single provider or lose the knowledge and capabilities they build over time. Open weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them. However their business requirements demand. And as organizations create value with AI, open weights allow them to own that value through self-improving models, specialized capabilities, and accumulated knowledge that drives American sovereignty and prosperity. To be sure, open weights carry real and distinct risks. Once released, the weights are beyond the original developer's control, and modified versions are difficult to trace or reverse. But the right response to this risk is not to prohibit open weights. In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats. Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams. In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe. They can be breached, misused, or even used for malicious purposes. Misused or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time. Just as open-source software demonstrated that transparency can be more secure than obscurity, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies. It allows for rigorous benchmarking and evaluation, red teaming, and protections tied to real and demonstrated harms rather than assuming that closed systems are safer by default. A strong AI ecosystem is not a foregone conclusion. Policymakers have an important opportunity to act. This includes expanding access to compute for startups and researchers, investing in shared training assets, datasets, tools, evaluation frameworks, and keeping the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas. These measures must also look at how strong application layers can expand sovereign use of AI across the economy. In shaping this ecosystem, policymakers should be careful not to conflate legitimate model development techniques with misappropriation. Distillation, or the practice of using one model's output to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation. The age of AI can be one of prosperity. With the right choices, open weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and ensure that the benefits of this extraordinary technology are shared broadly across our economy. That future is worth building, and the United States should lead in building it. Some of the signed or co-signed entities: American Innovators Network, Andreessen Horowitz, AI, RCI, Arena, Black Forest Labs, Box, Cisco, CrowdStrike, Dell Technologies, DoorDash, et cetera. That was a lot to read, but also quite concise for the directionality and thought leadership and concepts sort of conveyed by Jensen there. Clearly, he is for open weights, and Elon Musk actually also supported Jensen's proposal. Elon's tweet here, this has my full support. Jensen is right. Retweeting and showing his support of Jensen's very first post on X. Jonathan, what is your CTO perspective on Jensen Huang's first tweet and letter from NVIDIA here on why open models matter?

I mean, okay, let's talk about where American thought leadership is coming from right now, because I think that's an interesting story on its own. You know, I thought like the Chinese actually had clear thought leadership from their government, from Xi, from giving speeches. Like they're all kind of aligned on this open weights being a good thing for humanity as a whole. We are fighting about it in the United States. And like, there's kind of a vacuum of leadership from our administration. They don't really get it. And they're still, so like trying to scramble and come and figure out what's what and what they should regulate. So like more of a defensive posture. I think the leadership has been in Sam Altman's hands and Dario Amodei's hands. For like what the future should look like with AI. I feel like they dropped the ball. They went into like protect my company's valuation mode instead of like creating a narrative that all of us could buy into. And I think you saw remnants of this when Fable 5 came out with the Alex Karp rant, with the Satya Nadella letter about the frontiers. I think Jensen has now synthesized this and said like, you know, okay, from big tech company leadership standpoint, we can have more of a stakeholder-aligned view of like what leadership looks like and synthesized it into this document, you know, that protects consumer data and company data and it protects company interest. It makes a narrative about how America can be strong under this umbrella, right? So I think this was like a really positive example of American leadership coming through. Maybe it doesn't come from the place where you think, you know, we were talking about Alex Hormozi has this view of influence of like, well, it's the richest people in the world who are the most influential, especially under American, you know, and you can sort of see that like it took, you know, but de facto, like, like JP Morgan stuck stepped in, in, in the financial crisis of the, you know, late 1800s, you have people like Jensen Huang, the, the CEO of the most valuable company on earth, stepping up to like provide leadership. And that's, that's sort of an interesting Americanism of like where, how we promote our leaders, right?