For several weeks this past summer, the artificial intelligence sector was captivated by Anthropic's newest frontier models and the political struggle in Washington over who could access them. Yet amid all the attention on cutting-edge technology, developers continued building—and they weren't waiting for approval from industry giants like Anthropic or OpenAI. Chinese open-weight models accounted for 41% of all downloads on Hugging Face this spring, surpassing their U.S. counterparts. On OpenRouter, the six most popular models are all open-source offerings from Chinese companies such as Tencent, Xiaomi, DeepSeek, MiniMax, and Z.ai. Currently, Anthropic's Claude Opus 4.7 sits in seventh place. Data from Vercel reveals that open-weight models are handling much of the heavy infrastructure for AI applications, while closed models function as a pricier, premium tier. In June, open models managed nearly a third of AI requests on the platform. These platforms only capture a portion of the broader AI ecosystem—they don't include sessions hosted by major labs, which likely represent the bulk of usage for OpenAI and Anthropic. Still, the large and growing market share of open-source models raises a tough question: How relevant do frontier models remain if most production AI ultimately relies on cheaper, customizable alternatives?

Some observers view the rise of open-source models as evidence that the most intelligent systems may only serve niche, high-value tasks. "Maybe in a few years, the frontier models will be for experimenting and for some really high value tasks, and most of the production workloads will actually be powered either by private models within companies or by open source models," Hugging Face CEO Clem Delangue said on a recent episode of Equity.

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Hugging Face is a platform and developer community known for hosting, sharing, and helping businesses deploy open models. Delangue notes that the platform's customers and community members are increasingly highlighting the advantages of owning their own AI models rather than renting them—a trend that has gained momentum as companies face the steep costs of scaling closed frontier models. "If you're an AI company or a technology company, you don't want to outsource your core capabilities to another company, to a black box API that you don't control, don't have any visibility on, and don't really have any sort of ownership," Delangue explained. This shift, he argues, is evident in Hugging Face's activity: a new repository is created every seven seconds on the platform, which now hosts nearly three million public models and one million public datasets. That paints a different picture from the "one model to rule them all" narrative, Delangue says. Instead, it suggests companies are using many different models, many customized for specific needs. Half of all Fortune 500 firms are using Hugging Face to deploy their own private models and open-source models, he adds.

The growing popularity of open models coincides with a steady stream of increasingly capable releases from Chinese AI labs. Every few months, another Chinese AI company unveils a powerful open-weight model that is cheaper to deploy and easier to customize than its closed competitors, undermining the economics of proprietary AI that U.S. firms have invested billions in. Most recently, Beijing-based Z.ai released an open-weight model called GLM-5.2, which excels at agentic coding and competes with Anthropic's latest models on identifying security vulnerabilities. Delangue isn't alone in arguing that enterprises should avoid tying themselves to a single model provider. Microsoft CEO Satya Nadella recently warned against single-provider lock-in, emphasizing that data control should be a primary concern for businesses using AI. "While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data," Nadella said. "If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop."

The rise of open models has also intensified a debate over whether increasingly capable systems should be broadly available at all. Anthropic CEO Dario Amodei has argued that distributing powerful open-weight models could become dangerous because once released, they are hard to control. Others contend that open models are more accessible to bad actors who could use them to spread disinformation or engage in cyber or biological warfare. Delangue sees the tradeoff differently. "The biggest risk in AI is concentration of power," he said. "The way you make the world safer, in my opinion, is by leveling up the playing fields and creating transparency on these models." Transparency means defenders can more easily "patch the cybersecurity risks that they already know open source models can exploit," he added. The Hugging Face executive argues that keeping powerful models closed doesn't eliminate the risks associated with advanced AI systems, partly because it's easy to bypass frontier model API guardrails or steal the weights and release them publicly. Restricting powerful models, Delangue contends, simply concentrates technology in the hands of a few companies while reducing transparency into how systems work. "You don't really make it safe by keeping it behind closed doors for just a few players," he said. "You make it more dangerous because you create asymmetry of power and asymmetry of capabilities."