Mark Cuban warns that patenting work makes it public, allowing any AI model to train on it instantly. To maintain a competitive data advantage, he suggests companies should increasingly rely on trade secrets, keeping their valuable IP out of the public domain and away from competitors' models.

China is gaining an efficiency edge in AI by using "distillation"—training smaller, cheaper models from larger ones. This "train the trainer" approach is much faster and challenges the capital-intensive US strategy, highlighting how inefficient and "bloated" current Western foundational models are.

While US firms lead in cutting-edge AI, the impressive quality of open-source models from China is compressing the market. As these free models improve, more tasks become "good enough" for open source, creating significant pricing pressure on premium, closed-source foundation models from companies like OpenAI and Google.

The geopolitical competition in AI will decide the economic value of intellectual property. If the U.S. approach, which respects copyright, prevails, IP retains value. If China's approach of training on all data without restriction dominates the global tech stack, the value of traditional copyright could be driven toward zero.

The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.

The closed nature of leading US AI models has created an information vacuum. Sridhar Ramaswamy notes that academia is now diverging from US industry and instead building upon published work from Chinese companies, which poses a long-term risk to the American innovation ecosystem.

The "golden era" of big tech AI labs publishing open research is over. As firms realize the immense value of their proprietary models and talent, they are becoming as secretive as trading firms. The culture is shifting toward protecting IP, with top AI researchers even discussing non-competes, once a hallmark of finance.

US officials and AI labs allege Chinese firms are engaged in industrial-scale IP theft. They reportedly use fraudulent accounts to extract capabilities from US models like Claude to train their own, creating a facade of domestic innovation.

Leading Chinese AI models like Kimi appear to be primarily trained on the outputs of US models (a process called distillation) rather than being built from scratch. This suggests China's progress is constrained by its ability to scrape and fine-tune American APIs, indicating the U.S. still holds a significant architectural and innovation advantage in foundational AI.