China's AI ecosystem is producing real innovations in robotics and efficiency, but a closer look at the country's labs reveals a dependence on Silicon Valley templates and Western safety frameworks that complicates the narrative of Chinese AI independence. A fact-finding mission by prominent AI researchers and writers from the US and UK to nine major Chinese AI companies, including DeepSeek, 01.ai, Moonshot, and Alibaba Cloud, offers a nuanced view of where China stands in the global AI race.

What Are China's Genuine AI Breakthroughs?

China's AI labs are not simply copying Western work. The trip revealed legitimate technical achievements that deserve recognition. Unitree's robotics products, ByteDance's image diffusion models, and efficiency improvements across multiple labs represent genuine advances that have been largely open-sourced to benefit the broader research community. These are areas where Chinese companies have moved beyond imitation into meaningful innovation.

DeepSeek, in particular, has earned respect as a "respected technical leader" in China's AI ecosystem, according to observers on the trip. The company's model releases have captured global attention and positioned China as a credible force in large language model (LLM) development, which refers to AI systems trained on vast amounts of text data to understand and generate human language.

Why Do Chinese Labs Still Look to Silicon Valley?

Despite these achievements, the trip revealed a striking pattern: Chinese AI researchers and engineers remain heavily oriented toward Western technology and frameworks. Technicians at multiple labs were using Claude Code, a coding tool from Anthropic, despite the service being blocked in China. Some researchers even used English rather than Chinese for their coding prompts. This dependency extends beyond tools to fundamental approaches.

The reliance on Western models runs deeper than software choices. Researchers responsible for training AI agents told visitors they were taking safety cues from Western institutions like METR, a technical AI safety organization. When evaluating new models, lab staff referenced Western benchmarking services like Artificial Analysis, SemiAnalysis, and Interconnects, explicitly noting that Chinese evaluation benchmarks were not considered reliable.

The origin story of DeepSeek itself illustrates this dynamic. Before founding DeepSeek in 2023, the company's parent organization, High-Flyer, experimented with various frontier technologies including self-driving cars. It was only after OpenAI's ChatGPT became popular in late 2022 that CEO Liang Wenfeng decided to hire large language model researchers and pivot toward LLM development. Without that catalyst from California, DeepSeek might never have entered the AI models space at all.

How Do Ideological Controls Shape Different Chinese AI Models?

One of the most revealing findings from the trip concerns how Chinese AI models differ in their willingness to address sensitive topics. The variation appears to correlate with company size and government alignment rather than centralized control. Models from smaller startups like Z.ai and Moonshot were more willing to discuss contentious subjects, including China's human rights record, and produced fewer guided or censored responses. Meetings with these companies were notably informal, conducted over pizza or fried fish.

By contrast, models from larger, more established companies like Alibaba and DeepSeek showed stronger alignment with Chinese Communist Party values. Alibaba, as a behemoth with established reach and influence, has bureaucratic structures that naturally enforce ideological control. DeepSeek's ideological alignments visibly tightened after its leadership publicly endorsed the startup and it was adopted into government services.

This pattern suggests a structural tension within China's AI governance. The Cyberspace Administration of China (CAC), the body tasked with regulation and control, appears to be taking a relatively hands-off approach to allow the AI ecosystem to develop. Under CAC regulations from 2022, individual labs retain primary responsibility for information security, including ideological control. This decentralized approach may explain why different companies have adopted different levels of content filtering based on their own corporate cultures and market ambitions.

Steps to Understanding China's AI Governance Structure

• Regulatory Framework: The Cyberspace Administration of China (CAC) sets broad guidelines but allows individual labs primary responsibility for implementing ideological control and information security within their own organizations.
• Competing Priorities: A structural tension exists between the CAC's role enforcing Party values and the Ministry of Industry and Information Technology (MIIT) and National Development and Reform Commission (NDRC) pushing for sector reform and opening to international markets.
• Company-Level Variation: How strictly models enforce content control depends more on individual company culture, size, and international market ambitions than on centralized CAC directives.
• Western Influence: Chinese labs continue to adopt Silicon Valley templates, safety frameworks, and evaluation standards, suggesting that global AI governance norms are shaping Chinese development even as the country pursues independence.

The trip also revealed that Chinese AI companies are actively pursuing international expansion. At Beijing Capital International Airport, Alibaba Cloud advertises that it "gives all-out support to Chinese enterprises going overseas." One anonymous source met during the visit made clear the strategic intent: "Frankly speaking, apart from top American AI companies such as OpenAI and Anthropic, our experience in technical practice is ahead of these visiting scholars. This exchange is also a good opportunity to spread the influence of Chinese AI large-scale models overseas".

The source added that while "Chinese AI has a certain reputation in the industry," companies hope such exposure could lead to adoption by major international players like Apple and Walmart. This reveals that the trip itself was part of a broader international marketing strategy, with Chinese labs eager to make favorable impressions on influential Western AI experts.

The picture that emerges is neither one of Chinese AI independence nor Western dominance, but rather a complex ecosystem where genuine innovation coexists with structural dependence on Western frameworks, tools, and validation mechanisms. As China's AI sector continues to mature and pursue global markets, this tension between innovation and imitation, between ideological control and technological progress, will likely shape the future of AI development both within China and globally.