The accusation, reported by Ars Technica this week, is that six Chinese AI firms lifted outputs from US frontier models, Anthropic's and OpenAI's among them, to train their own systems rather than building from scratch. Call it what it is: distillation without a license, training a smaller model on a bigger one's answers. It's a real practice and a real dispute. But the more useful number is not how many firms got named. It's how many of them have an enterprise sales channel. As of this month, essentially none does. Alibaba Cloud and Tencent Cloud sell compute. Neither operates the kind of packaged, compliance-cleared AI product that a bank in Singapore can put in front of a loan officer next quarter. That's the gap that decides who profits from a good model, not who trained it.

Here's why that gap matters more than the lawsuit. A copied model is cheap to produce and hard to sell into a regulated institution, because the buyer isn't just buying weights, it's buying a vendor relationship: a signed contract, an integration into existing systems, and someone accountable when the Monetary Authority of Singapore asks how the model was validated. Anthropic and OpenAI have that relationship built through Azure and enterprise API contracts already live inside banks and insurers across Hong Kong and Singapore. A Chinese lab that copied GPT-6 Astra's outputs still has to build the sales team, the compliance paperwork, and the deployment track record from zero, and export controls on the H200 and equivalent hardware mean it's doing that build without the training compute the US labs use to keep improving the product it's supposedly copying. The accusation is about training data. The constraint that actually bites is that copying gets you a model, not a customer, and by the time any of these six firms builds a sales channel to match, the US labs it copied will have shipped two more model generations on hardware the copiers still can't buy.