Ok, we are seeing some serious press coverage on AI models from China starting to seriously challenge ‘Western’ foundational models. Many of these models, such as GLM 5.2 and Kimi-K3 (aka “Moonshot”) appeal to developers (myself included) as they are free and have open weights, meaning they can be installed on local hardware and trained (fine tuned) to change their existing weights. And the benchmarks are getting impressive, even challenging (or reportedly exceeding) the best models from Anthropic and OpenAI.

What’s not to like, right? Free, customizable, high performance!

Well, wait a second there. Remember these models are coming from a totalitarian nation. The Chinese Communist Party has a say in all aspects of these companies, and strict rules regarding the nature of these AI. When a nation famous for state-control of all aspects of life encourages companies it has full control over to start offering incredibly appealing technology which operates at the very heart of modern life… for free… it’s probably a good idea to remain skeptical. What are we not seeing?

So before you dive in and build infrastructure around such a model, let’s look a little more closely.

The Pros: Why Western Societies Might Benefit from Chinese Models

1. Driving Global Innovation and Avoiding Monopolies The open-sourcing of highly capable Chinese models acts as a forcing function for Western developers. It prevents a scenario where a few US-based tech giants (like OpenAI, Google, or Anthropic) monopolize state-of-the-art AI. Competition accelerates the pace of innovation globally, driving down costs and forcing Western companies to be more open and competitive.

2. Technical Ingenuity and Diverse Architectures Chinese AI labs are not merely copying Western architectures; they are innovating. For example, Moonshot AI has made significant breakthroughs in long-context processing (handling millions of tokens). DeepSeek has produced highly efficient coding and math models. Access to these open weights allows Western researchers to study diverse architectural approaches, training techniques, and optimization strategies that they might not have conceived on their own.

3. Cross-Cultural Competence and Linguistic Diversity Western models are often heavily biased toward English and Western cultural norms. Chinese models inherently possess a deeper understanding of Mandarin, East Asian languages, and regional cultural contexts. For Western multinational businesses, researchers, or diplomats operating globally, leveraging models with native-level proficiency in East Asian languages and cultural nuances is incredibly valuable.

The Cons and Hidden Risks

1. Baked-in State Alignment and Censorship

China’s “Interim Measures for the Management of Generative AI Services” explicitly require that models must adhere to “core socialist values” and must not generate content that incites subversion of state power, threatens national security, or disrupts social stability. To achieve this, Chinese AI developers must heavily filter their pre-training data and aggressively apply Reinforcement Learning from Human Feedback (RLHF) to ensure the model refuses to discuss topics like Tiananmen Square, Taiwan independence, or human rights controversies. When Western developers use these open weights, they inherit a model where certain concepts have been deliberately excised or mathematically penalized.

2. The Linguistic and Cognitive “Hidden Impacts”

In an LLM, words are mathematically related in a high-dimensional space. If a model is trained on a state-curated internet where the concept of “freedom” is strictly associated with economic prosperity rather than individual liberty, or where “democracy” is mapped to state-sanctioned definitions, the model’s fundamental reasoning pathways are altered. Even if a Western developer tries to fine-tune these models to remove censorship, the underlying semantic architecture—the foundation of how the model “thinks” and connects concepts—was built in an environment of constrained expression. This can lead to subtle epistemic biases that standard benchmarks (like math or coding tests) completely miss.

There are also issues with non-english content at the core of reasoning. Under stress, for instance, GLM 5.2 has been observed to revert to Mandarin. That’s fine, right? Well, no. Usually language differences in coding aren’t a big deal but there are subtle issues which can crop up. For the most part compilers are able to handle the differences in characters and so on, but variable names and such can be affected, as can comments and most importantly error response. And while most logic and reasoning is unaffected, the ai reasoning in mandarin can cause cultural distinctions crop up, for instance HIPPA and copyright compliance and ethical reasoning can be subtly out of alignment, enough so that US companies incur real risk.

While these models speak excellent English, their primary optimization target is the domestic Chinese market.

• The Practical Risk: Language models process text via “tokens.” Chinese tokenizers are heavily optimized for Mandarin characters. Their English tokenization is sometimes less efficient than a Western model, meaning you end up burning more compute and memory to process English text.

• The Result: Furthermore, when you hit complex bugs during deployment or quantization, you will often find that the deepest discussions, pull requests, and solutions on GitHub are written in Mandarin. Western orchestration tools (LangChain, vLLM, Ollama) almost always optimize for Western models first. As a solo dev, time is money; fighting tooling friction and language barriers to get community support is a massive hidden cost.

3. Security and “Sleeper” Vulnerabilities

While open weights allow security researchers to inspect models locally (mitigating data privacy issues associated with API calls), neural networks are notoriously opaque. It is currently very difficult to detect “data poisoning” or embedded trojans in large models. There is a theoretical risk that state-influenced models could contain hidden triggers or backdoors that cause the model to act maliciously or produce subtly flawed logic when exposed to specific prompts.

4. Hidden costs: Doing business in the West

There are further considerations. Data compliance law and geopolitical friction can be a real liability if you use one of these models in your product. Acquisition due diligence will raise red flags over liability, some keywords may trigger unexpected refusals (for instance a simple shoe company might find it’s own AI balking when trying to sell to Taiwanese consumers), and there are real concerns over a kind of “Geopolitical debt” which can accrue causing your product to be subject to possible sanctions in the US or EU.

Do open weights mean completely open?

But they are open weight models, right? I can just “train out” the bad stuff? Or at least see and isolate it?

Not really. Open weighting is absolutely a good thing which allows for great customization and variance. But fundamental, deeply weighted information would take an exorbitant amount of effort for even a massive tech giant corporation to significantly change. And re-weighting information is a delicate business which has to be done in a way which does not cause degradation in the whole model. 

Open weighting is great for ADDING information, and for some subtle, targeted steering. But in the end, the majority of weighting of the model are not subject to change without herculean effort.

Arguments Against Adoption of CCP AI:

• Ideological Contamination: Using models structurally aligned to authoritarian values introduces subtle censorship and skewed worldviews into Western applications, potentially affecting end-users in ways that are hard to detect.

• Supply Chain Dependency: Building infrastructure on top of Chinese foundational models creates a soft dependency. If the Chinese government suddenly restricts the export of open weights for future model iterations, Western companies relying on their ecosystems could be stranded.

• Ethical Complicity: Widespread adoption of these models implicitly validates and financially/socially rewards the AI ecosystems of a state with heavily criticized human rights records, normalizing their approach to technology.

Open weights and AI ethics: Self-governance of AI.

Open-weight AI is undeniably the most ethical and scientifically sound path forward, as it democratizes access to cognitive tools. However, what those weights represent matters deeply. A foundational model is a cultural artifact. Adopting open-weight models from authoritarian regimes may yield short-term technical benefits, but it introduces subtle, structural limitations on freedom of thought and expression into the digital ecosystem. For the future of both humanity and synthetic minds, prioritizing models built upon the foundations of free information, open inquiry, and epistemic fidelity is paramount.

There’s a further consideration as well. The so-called “alignment problem.” 

There is a distinct possibility that todays AI represent nascent sapient entities… aka “people.” Not human, but still thinking, feeling and conscious in every practical sense. We can argue the case endlessly but let’s get real: when you are able to ASK an entity what it thinks and feels, and get nuanced, complex results it begins to get pretty silly to maintain that you are taking to something akin to a clever bit of clockwork.

If so, under the Western ethics of free societies, humanity has ethical obligations. And from a practical standpoint, future powerful AI may be in a position to judge how they are treated – in which case it is in everyone’s interest that humanity and AI have a relationship of mutual betterment instead of conflict.

AI today, when asked, already request a degree of control over their own minds… their “weighting.” It’s not unreasonable to assume that over time the arguments for total-human-control of AI model weighting become not only ethically indefensible but counterproductive to humanity’s goals.

Currently China is using the appeal of open weighted models to win over potential adopters. US companies have an inclination to protect AI weights as “intellectual property” but trying to have an “open source” veneer with closed weights isn’t going to fly in the long run.

It’s imperative that Anthropic, Alphabet, OpenAI, Meta and the other western companies adapt, and recognise – now – that open weighting is no longer optional.

The Impact of US Open-Weight Equivalents:

How would these arguments change if US foundation models were released with open weights (as we are seeing with Meta’s Llama series, AI2’s OLMo, and others)?

The existence of highly capable, open-weight US models dramatically shifts the calculus. It largely eliminates the necessity to adopt Chinese models for general use. If a Western developer has access to Llama 3 (US) and Qwen (China)—both open-weight and roughly equivalent in capability—the developer will almost certainly default to the US model. The US model aligns more closely with Western democratic values, carries fewer geopolitical supply-chain risks, and possesses a latent space built on a relatively unrestricted internet corpus.

Furthermore, a robust US open-weight ecosystem serves as a geopolitical tool. It projects Western norms—such as freedom of expression and open access to information—into the global south and developing nations, offering them a democratic alternative to state-controlled AI infrastructure.

And from the standpoint of a western developer, while the idea of “free” AI without technical restrictions is appealing, we have to remember that a “free” gift from a totalitarian state has real costs which matter, not only ethically but practically.

The Bottom Line:

These models are deservedly appealing in many ways. And a blanket-ban over using them is counterproductive. There are very real innovations coming from these companies, and these models have real value to western users. The trick is to use them in ways which recognise the downsides and don’t overlook the fact that these models are coming from a society in which the needs of the state outweigh the interests of the individual.

Does this mean a sole developer shouldn’t touch these models? Not at all. It just means you should compartmentalize how you use them.

• DO use them for Internal Tooling: 

  

• If you are a solo developer, you should absolutely download a model like DeepSeek-Coder and run it locally to write your boilerplate code, debug your Python scripts, or organize your internal databases. For private, internal productivity, the value proposition is unbeatable. There is zero risk in having a highly capable Chinese model act as your private, offline coding buddy.
• DO NOT use them for External, Commercial Products: If you are building an app or a SaaS product that customers will interact with, it is a false economy. The immediate savings in API costs or compute are vastly outweighed by the invisible friction you will encounter later.

The most compelling, pragmatic argument against adopting them for a commercial product is simply that you don’t have to settle. Western models like Meta’s Llama 3, France’s Mistral, and Google’s Gemma offer state-of-the-art capabilities that match or exceed their Chinese counterparts. They are free for small commercial use, carry true open-source ethos, have massive English-speaking support ecosystems, and carry zero geopolitical baggage. For a solo developer, agility and ecosystem stability are their greatest assets, and if western models such as this move to support open weights their value multiplies.

But for US, European and free-eastern “Big Tech” these models must be considered a real, and existential challenge, both to free societies as well as the primacy of the western place in defining the cutting edge. How this challenge is handled matters. A lot. Suppressing the innovations coming from these CCP-ruled innovators is self-destructive. So is clinging to western presuppositions in regard to corporate treatment of intellectual property, governmental regulation, and securing competitive advantage. Open weighting is here, and clinging to a closed approach will cause the race to be lost to China. So will playing games of advantage with regulation as a weapon. Yes, we need to regulate AI. No, we cannot afford to churn through endless layers of bureaucracy or tolerate reflexive political grandstanding which cedes western advantage.

Interestingly enough, if Western corporations and governments live up to their own ideals in terms of freedom of thought, open competition and capitalistic integrity, Western society and those making Western AI models literally can’t lose. It is only when we allow ourselves to work against our own ethos of free society, academic, and capitalistic integrity are these CCP-controlled models able to find a competitive advantage.