For the last half-decade, the prevailing narrative in Silicon Valley has been one of absolute, unassailable dominance. The United States possesses the GPUs, the capital, and the talent. Everyone else is merely playing catch-up, drafting behind the aerodynamic wake of OpenAI and Google. That narrative just hit a wall.

A rigorous new study by researchers at MIT, Hugging Face, and others has analyzed the complete history of model downloads—2.2 billion of them—to trace where the actual power lies in the ecosystem. The results are not just surprising. They represent a fundamental inversion of the status quo.

According to the data, China has officially overtaken the United States in the global market share of open model downloads. In the last year alone, Chinese organizations captured 17.1% of the download market, surpassing the US share of 15.8%. — Read More

The United States and China are often said to be in a “race” with one another with respect to artificial intelligence. In a sense this is true, but the metaphor manages to miss almost all that is interesting about US-China dynamics in emerging technology. Today I’d like to offer some brief thoughts about how I see this “race” and where it might be headed.

All metaphors are lossy approximations of reality. But “race” is an especially inapt metaphor for this context. A race is a competition with clear boundaries and a clearly defined finish line. There are no such luxuries to be found here. Beyond the rhyme, “the Space Race” made intuitive sense because the objective was clear: landing humans on the Moon.

Stating that there is an “AI race” underway invites the obvious follow-up question: the AI race to where? And no one—not you, not me, not OpenAI, not the U.S. government, and not the Chinese government—knows where we are headed. — Read More

On September 11, the U.S. Federal Trade Commission launched an inquiry into seven tech companies that make AI chatbot companion products, including Meta, OpenAI, and Character AI, over concerns that AI chatbots may prompt users, “especially children and teens,” to trust them and form unhealthy dependencies.

Four days later, China published its AI Safety Governance Framework 2.0, explicitly listing “addiction and dependence on anthropomorphized interaction (拟人化交互的沉迷依赖)” among its top ethical risks, even above concerns about AI loss of control. Interestingly, directly following the addiction risk is the risk of “challenging existing social order (挑战现行社会秩序),” including traditional “views on childbirth (生育观).”

What makes AI chatbot interaction so concerning? Why is the U.S. more worried about child interaction, whereas the Chinese government views AI companions as a threat to family-making and childbearing? The answer lies in how different societies build different types of AI companions, which then create distinct societal risks. Drawing from an original market scan of 110 global AI companion platforms and analysis of China’s domestic market, I explore here shows how similar AI technologies produce vastly different companion experiences—American AI girlfriends versus Chinese AI boyfriends—when shaped by cultural values, regulatory frameworks, and geopolitical tensions. — Read More

There is now a path for China to surpass the U.S. in AI. Even though the U.S. is still ahead, China has tremendous momentum with its vibrant open-weights model ecosystem and aggressive moves in semiconductor design and manufacturing. In the startup world, we know momentum matters: Even if a company is small today, a high rate of growth compounded for a few years quickly becomes an unstoppable force. This is why a small, scrappy team with high growth can threaten even behemoths. While both the U.S. and China are behemoths, China’s hypercompetitive business landscape and rapid diffusion of knowledge give it tremendous momentum. The White House’s AI Action Plan released last week, which explicitly champions open source (among other things), is a very positive step for the U.S., but by itself it won’t be sufficient to sustain the U.S. lead.  — Read More

We keep hearing that China is catching up with the West in AI compute. A great example of this comes from NVIDIA’s CEO Jensen Huang, who recently claimed that China has made “enormous progress” in the last few years, and that “China is right behind us. We’re very, very close.”

And China has indeed been making a ton of progress. As we’ll see, Chinese hardware has been closing the gap across a range of metrics relating to computational power and data transfer, both of which are crucial aspects of AI workloads.

But despite progress on these metrics, we don’t think China is about to leap ahead of the West on AI compute. China’s top developers—including Alibaba, ByteDance, Baidu, and DeepSeek—still rely primarily on NVIDIA chips. And major bottlenecks still remain before China can leap ahead. — Read More

America is in a race to achieve global dominance in artificial intelligence (AI). Winning this race will usher in a new era of human flourishing, economic competitiveness, and national security for the American people. Recognizing this, President Trump directed the creation of an AI Action Plan in the early days of his second term in office. Based on the three pillars of accelerating innovation, building AI infrastructure, and leading in international diplomacy and security, this Action Plan is America’s roadmap to win the race. — Read More

#china-vs-us

The recent strides toward artificial general intelligence (AGI)—AI systems surpassing human abilities across most cognitive tasks—have come from scaling “foundation models.” Their performance across tasks follows clear “scaling laws,” improving as a power law with model size, dataset size, and the amount of compute used to train the model.¹ Continued investment in training compute and algorithmic innovations has driven a predictable rise in model capabilities.

In the manner that the architects of the atomic bomb postulated a “critical mass”—the amount of fissile material needed to maintain a chain reaction—we could conceive of a “critical scale” in AGI development, the point at which a foundation model automates its own research and development. A model at this scale would result in an equivalent research and development output to hundreds of millions of scientists and engineers—10,000 Manhattan Projects.²

This would amount to a “fourth offset,” a lead in the development of AGI-derived weapons, tactics, and operational methods. Applications would include unlimited cyber and information operations and potentially decisive left-of launch capabilities, from tracking and targeting ballistic missile submarines to—at the high end—developing impenetrable missile defense capable of negating nuclear weapons, providing the first nation to develop AGI with unprecedented national security policy options.

This means preventing the proliferation of foundation models at the critical scale would therefore also mean preventing the spread of AGI-derived novel weapons. This supposition raises the bar on the importance of counter-proliferation of the next stages of AGI components. AGI could also be used to support counter-proliferation strategy, providing the means needed to ensure models at this scale do not proliferate. This would cement the first-mover advantage in AGI development and, over time, compound this advantage into a fourth offset. — Read More

While America has the best AI models in Gemini, Claude, o3, etc. and the best infrastructure with Nvidia it’s rapidly losing its influence over the future directions of AI that unfold in the open-source and academic communities. Chinese organizations are releasing the most notable open models and datasets across all modalities, from text to robotics or video, and at the same time it’s common for researchers worldwide to read far more new research papers from Chinese organizations rather than their Western counterparts.

This balance of power has been shifting rapidly in the last 12 months and reflects shifting, structural advantages that Chinese companies have with open-source AI — China has more AI researchers, data, and an open-source default.

On the other hand, America’s open technological champions for AI, like Meta, are “reconsidering their open approach” after yet another expensive re-org and the political environment is dramatically reducing the interest of the world’s best scientists in coming to our country. — Read More

In China, U.S.-based large language models like ChatGPT, Claude, or Gemini are technically banned, blocked, or buried under layers of censorship. The Chinese government has only explicitly banned ChatGPT, citing concerns over political content, while other U.S. models like Claude and Gemini are not formally banned but remain inaccessible due to the Great Firewall. U.S. LLM providers also restrict access from China but leave some loopholes: OpenAI blocks API use but Azure continues to serve enterprise clients via offshore data centers; Anthropic blocks access to Claude within China but permits use by Chinese subsidiaries based in supported regions abroad; and Google does not offer the Gemini API in China, but access seems to be still possible via third-parties like Cloudflare (we reached out to Google for a comment but didn’t hear back).

But on Taobao, the country’s largest e-commerce platform, consumers and companies can buy access to these models with just a few clicks. This piece explains how Western models are priced, advertised, bought, and sold in China, and what their popularity reveals about state censorship, platform enforcement, and consumer demand. — Read More