in other ways. Uh and a very similar thing has always been happening, but I think it's becoming clear to people in in the space is that the the data that people get from from you using these models is how these companies largely make their models better, whether it's through actually training on that data or um by using it as a signal for what data they they go out and find or generate to train. And uh with closed models, there are terms of service that keep you from being able to um well, I could get into a a discussion about what terms of service is an agreement between you and the provider, it's not a legal uh anyway, but you you you shouldn't be training on a, you know, a Claude Opus output or a Fable output or a GPT-5 output, and they do a lot to try to obfuscate to to make that not great for you. If you're using an open model, you can save all of those traces. All of those traces of you using it inside of your harness uh that will allow you over time to if you say, "Hey, I want to go train a custom model," you can take all of that, and again, either use it to directly do like fine-tuning on a smaller model, so you're not spending as much, or to have a model help you find signals, so that you can go out and use Verifiers or Nemo RL or Nemo Gym to create these environments, so that you can hill climb and make your models better. So, um as much as using open models is like owning your stack, owning your intelligence, it's also owning your outputs, right? Owning your data. That's going to be extremely important, too.