The release of a powerful, US-based open-source model like Inkling is particularly well-timed. As Beijing considers curbing overseas access to its top AI models, a void is created for a trusted, "Western open source" alternative. This positions Thinking Machines to capture businesses wary of geopolitical risks or potential lock-outs associated with using Chinese models.

DeepSeek's V4 model, while not frontier-level, is drastically cheaper than US counterparts. This makes it highly attractive for most business use cases, creating a national security risk if US companies become dependent on Chinese-controlled, open-source AI infrastructure that could be altered or restricted, leaving them strategically vulnerable.

Unlike the US's increasingly closed-off AI models, China's powerful open-source alternatives (like Zhipu's GLM 5.2) are seeing massive global adoption. This strategy risks creating a world where Chinese AI is the global standard and US models are confined to the US and a few allies, effectively creating an "AI Iron Curtain."

The open vs. closed source debate is a matter of strategic control. As AI becomes as critical as electricity, enterprises and nations will use open source models to avoid dependency on a single vendor who could throttle or cut off their "intelligence supply," thereby ensuring operational and geopolitical sovereignty.

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.

Despite leading in frontier models and hardware, the US is falling behind in the crucial open-source AI space. Practitioners like Sourcegraph's CTO find that Chinese open-weight models are superior for building AI agents, creating a growing dependency for application builders.

Large American enterprises are in a difficult position, expressing terror about working with both frontier AI labs and Chinese open-source models. They fear the competitive risk and data privacy issues from labs like OpenAI, while also being wary of security vulnerabilities and geopolitical risk from Chinese models, creating a strong demand for a sovereign, trusted alternative.

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.