Why "Open Source AI" Is a Misleading Label, Explained

winded way of saying I don't think open source works the same way in in AI that it has worked in other areas primarily because with open source you can you can see the you know you can see the source code of the model here we can't see inside the model you know it's often called open weights instead of open source to kind of distinguish that but a lot of the benefits which is that many people can work on it that it's kind of additive it doesn't quite work in the same way So, you know, I've actually always seen it as a red herring. When I see a new model come out, I don't care whether it's open source or not. Like if we talk about DeepSeq, I don't think it mattered that DeepSeq is open source. I think I ask, is it a good model? Is it better than us at, you know, the things that we – that's the only thing that I care about. It actually doesn't matter either way because ultimately you have to host it on the cloud. The people who host it on the cloud do inference. big models they're hard to do inference on and conversely many of the things that you can do when you see the weights um uh uh you know we're increasingly offering on clouds where you can fine -tune the model you can you know um you know we're even looking at at ways to you know to to kind of you know investigate the activations of the model as part of like an interpretability interface we did some little things around steering last time um so i think it's the wrong access to think in terms When I think about competition, I think about, like, which models are good at the tasks that we do. I think open source is actually a red herring. But if it's free and cheap to run, I think. You have to run it on inference, and someone has to make it fast on inference.