China’s open-weights AI strategy is winning

AI models, as a product in themselves, have very little moat beyond what amounts to brand loyalty and superficial switching costs. Instead, the moat is in the enterprise services that sit around them: the deals and contracts, connectivity with enterprise systems, and quality of life features in an enterprise context.

If we consider the models themselves, it’s easy to switch between them: someone could be using ChatGPT today and Claude tomorrow, with very little impact on their workflows. This is particularly true in the engineering world, where models are accessed via API: you can swap out the API and use the same prompt.

Those companies can make deals to lock their customers in, but in practice there’s very little long-term technical incentive to use one vendor over another. You pick the best model for your needs and change models and vendors if another one becomes better.

The US government has placed export controls on GPUs. There are also strong regulations that (reasonably) prevent sharing certain kinds of data with Chinese servers. The result is that while Chinese companies have enough compute to train models, they can’t really provide the kinds of global-scale centralized services that we see from OpenAI and Anthropic — at least, not in the same way.

And open almost always wins when it comes to infrastructure adoption. Open technologies can be used permissionlessly and therefore can be at the center of more innovation. You can host them where you want, experiment with them, alter them, and tweak to fit your use case. Open weights models are not open source, but they are portable and permissionless.

With all this in mind, it makes sense for China to release its AI models openly. It turns a US-created compute disadvantage into a distribution advantage; it commoditizes the layer where American companies make money; and it creates a far more effective global ecosystem than could be established through locked-in, centralized services. It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models.

The saving grace for American companies has been that US frontier models have outperformed open ones. That gap is now closing :

Even without these new capabilities, the strategy has already been working. a16z partner Martin Casado noted in the Economist that there’s an 80% chance that any given startup is using Chinese models , and Chinese models are poised to take the lead .

It’s worth taking a step back and considering the surprising underlying dynamics. We think of China as being a locked-down society — and it is in many ways. I have serious concerns about how these models might reflect Chinese government perspectives ( try asking them about Tiananmen Square ). But it’s American companies that are keeping tight control of their technology rather than releasing it as openly as possible. This is in stark contrast to the strategy behind US government support for the open internet, for example.

Locked-down business practices for a technology with no real moat but significant potential ecosystem benefits is an obviously losing strategy; permissively releasing it with an open, collaborative approach is obviously a winning one. But the incentives in the US aren’t there: instead, these companies are forced to chase first-order profits rather than ecosystem benefits, and the government tries to put its finger on the scale through forcible measures like tight export controls. We should consider what would need to change to make those incentives more aligned. That’s particularly important given how much of the US economy is currently driven by AI spending . If the bottom falls out of that spending — and I think it clearly will, given the dynamics — the outcome could be severe.

I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI , federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.