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.

China gives away powerful AI models because it knows Western corporations won't subscribe to a closed, Chinese-hosted service due to trust and data security concerns. An open-source strategy allows for widespread adoption without requiring direct reliance on Chinese infrastructure.

AI company Clay uses powerful open-weight models from overseas for sensitive applications. They manage security concerns not by avoiding the models, but by hosting them with stateside inference providers like Base 10 and Fireworks, keeping the data and processing within the U.S. and under their control.

To manage high operational costs, some American AI startups adopt a hybrid approach. They build the bulk of their applications on performant, cheaper Chinese open-source models, reserving expensive frontier US models for critical tasks like evaluation and guidance.

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.

In the vacuum left by banned US frontier models, Chinese labs are releasing powerful and cost-effective open-source alternatives like ZAI's GLM 5.2. These models are proving competitive on valuable, complex tasks like UI design and coding, but at a fraction of the cost.

Geopolitical tensions aren't stopping US companies from adopting Chinese open-source AI models like Quen. The practical benefits of lower costs and faster fine-tuning are overriding political concerns, demonstrating that a true AI decoupling is difficult when economic incentives are strong.

The United States lacks a coherent national strategy for open-source AI, while China is rapidly producing high-quality models. This has created a situation where American companies are increasingly turning to Chinese-developed models to make their AI pipelines more efficient and competitive.

While US-based companies lead in closed, API-accessible frontier models, Chinese developers are the current powerhouse for high-performing open-weight models. For organizations wanting to self-host sophisticated AI, Chinese models are often the best available option.