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.
Related Insights
The exaggerated fear of AI annihilation, while dismissed by practitioners, has shaped US policy. This risk-averse climate discourages domestic open-source model releases, creating a vacuum that more permissive nations are filling and leading to a strategic dependency on their models.
Universities face a massive "brain drain" as most AI PhDs choose industry careers. Compounding this, corporate labs like Google and OpenAI produce nearly all state-of-the-art systems, causing academia to fall behind as a primary source of innovation.
Counterintuitively, China leads in open-source AI models as a deliberate strategy. This approach allows them to attract global developer talent to accelerate their progress. It also serves to commoditize software, which complements their national strength in hardware manufacturing, a classic competitive tactic.
Challenging the narrative of pure technological competition, Jensen Huang points out that American AI labs and startups significantly benefited from Chinese open-source contributions like the DeepSeek model. This highlights the global, interconnected nature of AI research, where progress in one nation directly aids others.
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 AI competition is not a simple two-horse race between the US and China. It's a complex 2x2 matrix: US vs. China and Open Source vs. Closed Source. China is aggressively pursuing an open-source strategy, creating a new competitive dynamic that complicates the landscape and challenges the dominance of proprietary US labs.
The initial fear around DeepSeq was about China surpassing US AI capabilities. The lasting, more subtle impact is that it broke a psychological barrier, making it commonplace for American developers and companies to adopt and build upon powerful open-source models originating from China.
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.
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.
While the U.S. leads in closed, proprietary AI models like OpenAI's, Chinese companies now dominate the leaderboards for open-source models. Because they are cheaper and easier to deploy, these Chinese models are seeing rapid global uptake, challenging the U.S.'s perceived lead in AI through wider diffusion and application.
