Despite security concerns, US companies might adopt Chinese open-source models like GLM because they can be hosted on US hardware with no data leakage. The immense cost savings and ability to maintain full control over the stack make them a practical alternative to expensive, risky frontier models.

A growing number of companies, especially in regulated industries like finance and healthcare, are opting for open-source AI models they can run on-premise. This trend is driven by concerns over data leakage, IP security, and national data sovereignty, creating a distinct market need for more domestic, controllable AI solutions separate from frontier models.

Most nations' sovereign AI strategies will not involve creating frontier models from scratch. Instead, they will adopt the best open-source models, customize them with local data and values, and run them on-premise for national security.

The distinction between "open-source" and "open-weight" is critical. Without access to the training data, users cannot know what biases or censorship have been built into an AI model. DeepSeek's pro-China stance on Taiwan is a clear example of this hidden influence.

China remains committed to open-weight models, seeing them as beneficial for innovation. Its primary safety strategy is to remove hazardous knowledge (e.g., bioweapons information) from the training data itself. This makes the public model inherently safer, rather than relying solely on post-training refusal mechanisms that can be circumvented.

A common misconception is that Chinese AI is fully open-source. The reality is they are often "open-weight," meaning training parameters (weights) are shared, but the underlying code and proprietary datasets are not. This provides a competitive advantage by enabling adoption while maintaining some control.

To clarify the ambiguous "open source" label, the Openness Index scores models across multiple dimensions. It evaluates not just if the weights are available, but also the degree to which training data, methodology, and code are disclosed. This creates a more useful spectrum of openness, distinguishing "open weights" from true "open science."

With frontier models, creators deny responsibility for user applications, while users claim no control over the model's inner workings. Sovereign AI eliminates this gap. By controlling the entire stack, an organization becomes fully accountable, satisfying regulators who need proof of what an AI did and why.

A common misconception about "open weight" models is that they are entirely free to use. While the model weights are publicly available for download, allowing for self-hosting and fine-tuning, their specific licenses vary and may restrict commercial use. Users must verify the license before deploying in a commercial setting.