A) Well, sure, yes, it's different specific IP being distilled on versus what was trained on. But I don't see why the same principles should not apply to both. If companies ignore IP when training on material, then it should be okay for other companies to ignore IP when distilling on material — either IP is a thing we care about or it isn't. (I don't).
B) I'm really not sure how seriously I take the worries about safety and security RLing models. You can RLA amodel to refuse to hack something or make a bio weapon or whatever as much as you want, but ultimately, for one thing, the model won't be capable of helping a person who has no idea what they're doing. Do serious harm anyway. And for another thing, the internet already exists for finding information on that stuff. And finally, people are always going to build the jailbreak models anyway. I guess the only safety related concern I have with models is sychophancy, and from what I've seen, there's no clear trend where closed frontier models are less sychophantic than open source ones. In fact, quite the opposite, at least in the sense that the Kimi models are significantly less psychophantic than everyone else.
C) This is a pretty fair point. I definitely think that having more base frontier models in the world, trained separately based on independent innovations, would be a good thing. I'm definitely in favor of having more perspectives.
But it seems to me that there is not really much chance for diversity in perspectives when it comes to training a base frontier model anyway because they're all already using the maximum amount of information available. So that set is going to be basically identical.
And as for distilling the RL behaviors and so on of the models, this distillation process is still just a part of what the Chinese labs do — they've also all got their own extensive pre-training and RL systems, and especially RL with different focuses and model personalities, and so on.
They've also got diverse architectures and I suspect, in fact, very different architectures from what's going on under the hood from the big frontier labs, considering, for instance, we're seeing DSA and other hybrid attention systems make their way into the Chinese model mainstream and their stuff like high variation in size, and sparsity, and so on.
D) I find that for basically all the tasks that I perform, the open models, especially since K2T and now K2.5, are more than sufficient, and I'd say the kind of agentic coding, research, and writing review I do is both very broad and pretty representative. So I'd say that for 90% of tasks that you would use an AI for, the difference between the large frontier models and the best open weight models is indistinguishable just because they've saturated them, and so they're 90% equivalent even if they're not within 10% in terms of the capabilities on the very hardest tasks.