The early rapid expansion of AI capabilities that focused on frontier models was largely ushered into the world by a few powerful, US-based AI labs. Open-weight models released from labs in China, early on from DeepSeek, and later from Moonshot, Z.ai, and others, have in part disrupted that dominance. But growing concerns about the concentration of power have led to discussions about the need for at least some level of AI sovereignty.
AI sovereignty doesn’t necessarily imply total control of your AI stack. It holds the promise of having more localized security and privacy, better adherence to local jurisprudence (for example EU AI and data laws), more dependable service, and potentially more culturally specific outputs from the AI technologies used within a specified border or region. Negotiating interdependence is not necessarily a problem, but having an array of tools beyond just open-weight models and options in those negotiations beyond just commercial offerings is imperative.
Unsurprisingly, the same AI labs that gave rise to the need for AI sovereignty are also pushing their own solutions to the problem. While local institutions consider and even adopt some of these initial “sovereignty” offerings from these labs, open source AI technologies may offer a more promising horizon, more flexibility, and a means to manage dependencies. Tim O’Reilly argues open source AI is a potential opening for greater participation in the future of AI’s development.
From the underlying chip technology that is necessary for model training and inference to the cloud and data infrastructure that enables model development, companies such as NVIDIA, OpenAI, Google, Microsoft, and AWS have begun to stake out their own territory to maintain relevance within the global push toward AI sovereignty. Stanford University’s Human-Centered Artificial Intelligence Lab (HAI) lays out the different approaches and offerings these labs have developed in its report The Commercial Landscape of AI Sovereignty Offerings. It argues that while these labs “promise that countries will own their AI stack, [they also] deepen dependencies on U.S. Big Tech.”
There are some non-US-based commercial alternatives that offer their own “full stack” solutions or AI sovereignty for specific layers of the stack. Companies in Europe, the Gulf region, Asia, and elsewhere are positioning themselves as local alternatives to US tech oligarchs. According to the HAI report, “Many of the most mature and advanced companies are actively backed by their governments. In these cases, sovereignty is not just a marketing claim but a stated policy objective, with governments directing funding, structuring procurement, and, in some cases, selecting specific companies to build out domestic AI capacity on their behalf.”
These types of collaboration can both enable independence from US labs but may also create openings for political intervention. Claims about censorship and control of Chinese models emerged quickly after DeepSeek’s initial 2025 release. More recently, there have been probes into how US models may limit certain types of discourse. Moreover, the HAI report points out that often the offerings of these alternative providers still rely on the underlying technologies, specifically chips and cloud infra, of the US labs.
The proliferation of commercial offerings provides the space for diversification or potential leverage to negotiate better terms for collaboration, even with the dominant players. Open source AI technologies also play an important role in creating opportunities for even more diversification and greater sovereignty. As HAI argues, “Sovereignty strategies that do not consider the role of open-source AI risk normalizing fragmentation and political overreach.”
In order for open source AI to counter the diversification of commercial sovereignty offerings, these technologies must also proliferate beyond open-weight models. Arguing for a “federated system” of open source AI that enables sovereignty based on an “architecture of participation,” Tim O’Reilly writes that “the right infrastructure to let us satisfy both goals [of being everywhere and allowing everyone to have a say] will be a federation of models, a federation of protocols and code, and a federation of capacity. We need an architecture of participation all the way down the stack, and all the way up.” A key technology in the expansion of the open source AI stack these days are agent harnesses.
In a recent article, Mozilla CTO Raffi Krikorian argues that “the orchestration layer above the [model] weights is where capability is concentrating, and closed labs are already welding it shut”; therefore, it’s imperative to build on open harnesses, not just models. Commercial offerings that have dominated thus far include Claude Code and Codex. OpenClaw offered an initial disruption and promise for open source in late 2025, though the creator was quickly absorbed into OpenAI’s organization. While big tech labs continue to absorb when, who, and what they can, NousResearch’s self-improving Hermes agent harness has also garnered substantial attention now with over 230,000 stars on GitHub. More recently harnesses such as Pi and DeepSeek Harness are expanding that open source offering, heeding Krikorian’s call.
Beyond agent harnesses, some of the strongest open source projects are developing in the less visible layers. Inference engines such as vLLM, SGLang, llama.cpp, and ONNX Runtime make it possible to serve a range of models efficiently across data centers, regional clouds, personal computers, and edge devices. Ray, which was developed by researchers at UC Berkeley, distributes demanding AI workloads. Ollama lowers the barrier to running models locally. Together, these projects give institutions more freedom to change models, hardware, and hosting providers without rebuilding an entire system around another company’s proprietary platform.
Other fast-growing projects are filling out the data, interoperability, and accountability layers of the stack. The Model Context Protocol and Agent2Agent Protocol offer open standards through which agents can connect to tools and to one another. Qdrant, Chroma, Milvus, and LanceDB provide open infrastructure for storing and retrieving institutional knowledge. MLflow, Opik, and OpenLLMetry allow developers to evaluate, trace, and monitor AI applications without surrendering operational data to a closed dashboard. The AI Potluck Gap Map classifies inference, deployment, and agent protocols as mature open ecosystems but identifies resiliency gaps in storage and observability, where fewer fully open projects occupy the leading tier. These gaps point toward an important investment agenda. Sovereignty will depend not on finding a single open replacement for Big Tech but on sustaining interoperable public alternatives across every consequential layer of the stack.
Even still, the looming threat of acquisitions and absorption of open source is persistent. The fintech company Stripe recently bought OpenRouter, a platform that allows developers to access various models and has become a primary hub for accessing and routing open-weight models in particular. NVIDIA has acquired Hugging Face, one of the key players for the open source AI ecosystem, and it has also just settled a licensing deal with Poolside, which develops open-weight coding models, purportedly to avoid the oversight of complete acquisition.
AI sovereignty may mean the necessity of “calibrating interdependence” with US Big Tech solutions or replacing a foreign dependency with a domestic one for the time being. But it must also entail building the technical capacity, open infrastructure, and participatory institutions needed to preserve genuine choice across the entire AI stack. Open source will continue to be vulnerable to commercial absorption, and efforts to counter that must become more robust.
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