One Year Since the “DeepSeek Moment”

This is the first blog in a series that will examine China’s open source community’s historical advancements in the past year and its reverberations in shaping the entire ecosystem. Much of 2025’s progress can be traced back to January’s “DeepSeek Moment”, when Hangzhou-based AI company DeepSeek released their R-1 model.

The first blog addresses strategic changes and the explosion of new open models and open source players. The second covers architectural and hardware choices largely by Chinese companies made in the wake of a growing open ecosystem, available here . The third analyzes prominent organizations’ trajectories and the future of the global open source ecosystem, available here .

For AI researchers and developers contributing to and relying on the open source ecosystem and for policymakers understanding the rapidly changing environment, there has never been a better time to build and release open models and artifacts, as proven by the past year’s immense growth catalyzed by DeepSeek. Notably, geopolitics has driven adoption; while models developed in China have been dominating across metrics throughout 2025 and new players leapfrogging each other, Western AI communities are seeking commercially deployable alternatives.

As seen in Hugging Face’s heatmap data , between February and July 2025, open releases from Chinese companies became noticeably more active. Baidu and Moonshot moved from primarily closed approaches toward open release. Zhipu AI ’s GLM and Alibaba’s Qwen went a step further, expanding from simply publishing model weights to building engineering systems and ecosystem interfaces. At this stage, comparing raw model performance alone was no longer enough to win. Competition increasingly centered on ecosystems, application scenarios, and infrastructure_._

The second was the adoption barrier. R1 was released under the MIT license, making its use, modification, and redistribution straightforward. Companies that had relied on closed models began bringing R1 directly into production. Distillation, secondary training, and domain-specific adaptation became routine engineering work rather than special projects. As distribution constraints fell away, the model quickly spread into cloud platforms and toolchains, and community discussions shifted from “which model scores higher” to “how do we deploy it, reduce cost, and integrate it into real systems.” Over time, R1 moved beyond being a research artifact and became a reusable engineering foundation.

DeepSeek has been heavily adopted in global markets, especially in Southeast Asia and Africa. In these markets, factors such as multilingual support, open-weight availability, and cost considerations have supported enterprise use.

Before R1, China’s AI industry was still largely centered on closed models. Open models had existed for years, but they were mostly confined to research communities or used only in niche scenarios such as privacy-sensitive applications. For most companies, they were not the default choice. Compute resources were tight, and whether to “open or close” was a subject of debate.

DeepSeek’s R1 model lowered the barrier to advanced AI capabilities and offered a clear pattern to follow, unlocking a second layer. Moreover, the release gave Chinese AI development something extremely valuable: time. It showed that even with limited resources, rapid progress was still possible through open source and fast iteration. This approach aligned naturally with the goals set out in China’s 2017 “AI+” strategy: combining AI with industry as early as possible, while continuing to build up compute capacity over the long term.

One year after the release of R1, what we see emerging is not only a collection of new models, but also a growing organic open source AI ecosystem.