This week, researchers at the Beijing Academy of Artificial Intelligence (BAAI) announced the release of Wu Dao 2.0, a multimodal AI model capable of generating text indiscernible from human-crafted prose -- and more. Containing 1.75 trillion parameters, the parts of the machine learning model learned from historical training data, Wu Dao 2.0 is 10 times larger than OpenAI's 175-billion-parameter GPT- 3. Wu Dao 2.0 is the latest example of what OpenAI policy director Jack Clark calls model diffusion, or multiple state and private actors developing GPT-3-style AI models. For example, Russia and France are training smaller-scale systems via Sberbank and LightOn's PAGnol, while Korea's Naver Labs is investing in the recently created HyperCLOVA. Clark notes that because these models reflect and magnify the data they're trained on, different countries care about how their own cultures are represented in the models. The Wu Dao 2.0 announcement, then, is part of a general trend of nations asserting their own AI capabilities via training frontier models like GPT-3.