Why does China open source their AI models?

A curious approach to AI competition

It’s a bit weird that anyone would open-source their models.¹ You are just giving away a thing cost millions of dollars and many researcher tears. Even Meta doesn’t do it anymore. Why does China?

1. Lack of inference capacity

U.S. export controls on advanced chips have made it hard for China to develop enough inference capacity to serve hundreds of millions of global users quickly. Earlier this week, the Chinese AI company Zhipu AI restricted sign-ups for their AI coding tool due to compute constraints.

Releasing models open-source instead of through an API means that Chinese AI developers don’t need to invest massive amounts to build out their own inference capacity, because users run the models on their own hardware (or third-party providers like Fireworks AI and Together AI step in to serve the models commercially, capturing the inference workload that Chinese labs can’t handle themselves).

There are signs China’s inference capacity is increasing, as it develops more domestic semiconductor supply chains — some suspect the supply to exceed demand as early as 2028.

Export controls under President Trump have also loosened. NVIDIA’s powerful H200 chips are designed for both training and inference, and the U.S. government greenlit their sale to China in December, despite calls to limit the country’s access to inference-capable hardware. The U.S. also allowed the sale of H20 chips back in August.

As this constraint becomes increasingly obsolete, Chinese labs face a different problem: even with sufficient inference capacity, they can’t charge more for models that lag behind Western ones.

2. Profit motive (“Commoditize your complement”)

While companies like DeepSeek occasionally demonstrate competitive performance on specific tasks, they haven’t achieved the kind of sustained superiority that would justify premium pricing.

So if you can’t monetize through paid API subscriptions because your product isn’t competitive enough, how do you extract value from your AI development?

Open-sourcing becomes the logical alternative: give away the weights to maximize adoption, then monetize through services rather than the model itself– enterprise support, fine-tuning, infrastructure– while building brand recognition for when you eventually catch up on capabilities. This is the Red Hat/Linux playbook: give away the core product, sell the services around it.

The profit motive problem is uniquely difficult to solve because as long as Chinese models lag behind U.S. ones, offering incentives like licensing deals or preferential market access won’t be compelling– why would a Chinese lab accept licensing restrictions to access U.S. markets when their model isn’t good enough to capture significant market share anyway? The closed-model revenue opportunity only becomes attractive if they believe they can actually compete.

Until Chinese models achieve genuine parity or superiority, commercial incentives to stay closed are weak because the foregone revenue is minimal.

3. Strategic disruption

By releasing state-of-the-art models for free, Chinese AI companies force Western competitors to either match the openness– eroding their pricing power– or justify premium pricing when “good enough” is offered for free. This commoditizes the base model layer, potentially reshaping industry economics in ways that favor Chinese players with lower cost structures.

A similar dynamic is currently playing out in the steel market: China’s “steel dumping” practices have made it virtually impossible for steel manufacturers worldwide to compete, since Chinese companies receive government subsidies and are thus able to offer steel on the international market for artificially low prices.

In both cases, China is pursuing an aggressive form of price competition that undermines the economic power of competitors.

4. Signaling & undermining export controls

The release of competitive open-source models, whether intentionally or not, serves Chinese strategic interests by demonstrating that U.S. chip export controls haven’t prevented frontier AI development. DeepSeek’s success signals China’s technological resilience and undermines confidence in export controls as an effective containment strategy.

5. AI diffusion and geopolitical influence

Additionally, by open-sourcing, China arguably gains soft power in defining AI architectures and potentially creates vendor lock-in to their ecosystems. In a future where the world runs on Kimi K2.5 instead of Claude, China plays a much more substantive role in shaping global AI development and adoption frameworks, undermining the power of the West. Imagine the Malawi government is consulting Kimi instead of Claude on national policy.

6. Reduced liability for misuse
Open-sourcing creates legal ambiguity around liability by decreasing the probability of attribution and diluting causation arguments. For example, if someone uses a closed DeepSeek API to design a bioweapon, there are logs, payment trails, and account information; meanwhile, if someone downloads open weights and uses them offline, there’s no record DeepSeek can be forced to produce.

With open weights, Chinese AI labs’ role goes from “service provider” to something closer to “information publisher,” making it much harder to establish direct causation– someone else modified the AI, someone else deployed it, someone else used it for harm.

Responsibility is also distributed between several different platforms that help host and serve models to users, such as Hugging Face and Fireworks AI. Fragmentation across the ecosystem makes any single actor harder to hold accountable.

Conclusion

While Chinese labs have various technical and commercial justifications for open-sourcing, the actual benefits appear negligible at best compared to the existential risks that open-source models pose. Moreover, open weights eliminate China’s ability to control content, monitor usage, or leverage AI access for geopolitical gain– undermining the CCP’s own goals.

In the next post, we’ll explore a series of interventions designed to stop or slow Chinese open-sourcing, targeting pressure points from commercial incentives and CCP self-interest to market restrictions and supply-chain regulations.

Thank you to Alex Waldherr for informative discussion.

1. We know it’s technically “open weight”, not open-source — but we’re trying to be understandable here.