American AI May Not Survive Chinese Open-Source
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Roadtripwithraj/CCTV Headquarters in Beijing
The AI industry has been rocked by recent large-language model (LLM) releases from Chinese labs, most notably the Kimi K3 model from Moonshot. K3 performed similarly to Anthropic’s Fable model on AI benchmarks, and its release follows the established norm among Chinese labs of being open-weights—that is, the model itself is given away online for others to run on their own hardware. The release of K3 came at an especially notable time, just a week after Anthropic’s Fable model was re-released to the public following a U.S. government restriction over concerns about its potential use as a cyber weapon. To release an open-weight model, completely lacking active monitoring and controls, that is approximately as powerful as a proprietary model that the U.S. government is treating like something just shy of a nuclear weapon was a stunning blow to the U.S. AI regulatory regime.
Chinese AI labs receive significant state support but are still heavily constrained in both financial resources and access to advanced chips compared to U.S. AI labs. There’s much debate in the AI industry about how a Chinese lab was able to pull off this remarkable coup with far fewer resources. A leading theory for this overperformance is distillation—that instead of training on text data from the internet, books, and other (oftentimes expensive) sources, Chinese models have been trained on prompts and responses from superior American models directly. This process allows the distiller to create a model that accurately mimics the source model at a fraction of the training cost.
Creating new models requires immense investment in research and compute, and in the United States this cost is financed by private investment with the primary goal of serving civilian demand. Unsurprisingly, these models have also found ample military applications in both kinetic and cyber use cases. LLMs have now been used for targeting purposes in Iran, and the CYBERCOM AI budget increased 2,660 percent in a single year as published doctrine has caught up, treating AI cyber capabilities as a core state capacity.
The United States’ lead in AI is likely to become an increasingly key strategic defense advantage as model applications diffuse past supporting operational intelligence and cyber warfare. As an industry, AI massively impacts every other industry through dramatic upscaling of R&D productivity, and defense technology is no different. Increasingly, the nation-state with access to the best AI will have the decisive advantage that begets the most advanced naval vessels, fighter jets, and drone technology. Furthermore, the specter of recursive self-improvement (RSI), a practice in which AI models directly drive R&D on future AI models at a level beyond what human researchers can perform, makes AI a unique security technology in that its advantage increases over time. Atomic weapons and stealth technology were temporary advantages that eroded quickly. The first country to reach RSI may find itself with a permanent decisive advantage. Their models may “take off” in capability as RSI models continue to train new, more powerful RSI models. The cyber warfare capabilities of such models could even prove to be a weapon that disrupts other states’ ability to train their own models.
In this light, AI begins to seem like a permanent strategic advantage technology that must be pursued at nearly any cost. If such assumptions hold, then the situation of such technology currently being financed by the private sector for applications such as assisting programmers and providing recipes is an almost comical mistake of history.
Viewed in this light, the Chinese open-weight strategy is fascinating. If one models China as a homogeneous and rational actor, the strategy of distilling American AI models and giving away the weights for free can be viewed as a brilliant tactic to kneecap a foreign industry rushing towards permanent strategic advantage. One could debate whether open-weights releases qualify as dumping in a trade sense or if they represent an open-source commoditization play—Chinese labs giving away a layer that they cannot hope to monopolize—but the outcome is potentially devastating to our domestic AI industry regardless. The cost of researching and training models can be over half of compute costs at major labs, with inference (the cost of serving models to users) making up the remainder. A firm that can skip training costs and simply charge for inference using an open-weight model is at a distinct economic advantage compared to labs such as OpenAI and Anthropic that must also bear the cost of development. This challenge to frontier lab economics is not just a matter of concern for those firms and their investors. Destroying American labs’ incentives to invest in AI model development may have major implications for U.S. military supremacy going...