I've previously discussed the concept of model collapse, and how feeding synthetic data (training data created by an AI, rather than a human) to an AI model can end up teaching it bad habits, but it seems that DeepSeek succeeded in training its models using generative data, but specifically for subjects (to quote GeekWire's Jon Turow) "...like mathematics where correctness is unambiguous,"

That sound you hear is me pressing F to doubt. Checking the correctness of mathematics written as prose interspersed with equations is, shall we say, not easy to automate.

OpenAI can't simply "add on" DeepSeek to its models, if not just for the optics. It would be a concession. An admittal that it slipped and needs to catch up, and not to its main rival...

I actually disagree here. I think Ed underestimates how craven and dishonest these people are. I expect they'll try to quietly integrate any efficiency improvements they can get from it and bluster through any investor questions about it. Their hope at this point has to be that more hardware is still better and that scaling is still gonna be the thing to make fetch happen. This again isn't a revolutionary new structure, even if it is a significant improvement over anything Saltman and co have been doing.