Lots of talk over the past week about American data companies selling training data to Chinese AI labs, and why that leaves the US behind. Up front: Scale doesn't do this work, and we've turned down revenue over it. A lot of it comes down to "isn't this just labeling?" Fair question, and the answer is no, but I want to explain why… Frontier post-training data has very little in common with annotation at this point. It looks more like curriculum design for training models. You're deciding which problems a model should struggle with, how a reasoning trace should be structured, the difference between a right solution and a lucky one, and what to reward. Tasks, RL environments and Verifiers are that same judgment but in executable form. Which means what’s being sold is a set of research decisions, already made and already validated. That's also why it's not a small business for anyone involved, and it’s why the few companies like us in the space are doing well. We build frontier data pipelines from our research and build OTS datasets to match because buyers want ready-made inventory and capability gains. Not coincidentally, Chinese labs' biggest jumps are in exactly the domains where sellable environments exist: agentic coding, math, tool use, etc. A few things about this that I don't think got much attention: ➡️ Compute controls assume compute is the binding constraint. But RL post-training is bound by reward signal, not just FLOPs - a lab that buys verified environments doesn't waste compute on failed exploration, so it reaches the same capability on far fewer chips. That's a substitution the export regime doesn't account for. ➡️ Functionally, this hands labs distillation on easy mode, minus the legal exposure. The verifier confirms which teacher outputs are actually right, so you distill from verified outputs. And dense reward signals cut the compute RL burns on failed exploration. ➡️ The judgment being sold is American expert judgment. The people writing these rubrics, the domain PhDs and engineers and professionals, generally have no visibility into who the end buyer is. They think they're contributing to work they'd endorse. ➡️ As recently reported some of the same vendors hold US government contracts. That's not a hypothetical conflict, and it should be getting more scrutiny. We shouldn’t assume bad intent from anyone. This is a supply chain that grew faster than our ability to think about what the rules should be. If you're buying data, "what am I getting" is only half the diligence. Where else that pipeline goes is the other half, particularly from companies that talk publicly about American AI leadership.

American companies should not be selling data to Chinese AI labs. @scale_AI doesn’t do this work, and we have turned down revenue because of it. The companies that do are undermining American AI leadership and risking national security.

Proud to support this on behalf of @scale_AI Open models are critical to American AI leadership and to building reliable AI. Great to join so many across the industry in signing on.