Sell it to whom, Ben?

Well, OpenAI has clearly scraped everything that is scrap-able on the internet. Copyrights be damned. I haven’t actually used Deep seek very much to make a strong analysis, but I suspect Sam is just mad they got beat at their own game.

The real innovation that isn’t commonly talked about is the invention of Multihead Latent Attention (MLA), which is what drives the dramatic performance increases in both memory (59x) and computation (6x) efficiency. It’s an absolute game changer and I’m surprised OpenAI has released their own MLA model yet.

While on the subject of stealing data, I have been of the strong opinion that there is no such thing as copyright when it comes to training data. Humans learn by example and all works are derivative of those that came before, at least to some degree. This, if humans can’t be accused of using copyrighted text to learn how to write, then AI shouldn’t either. Just my hot take that I know is controversial outside of academic circles.

Yah, I’m an AI researcher and with the weights released for deep seek anybody can run an enterprise level AI assistant. To run the full model natively, it does require $100k in GPUs, but if one had that hardware it could easily be fine-tuned with something like LoRA for almost any application. Then that model can be distilled and quantized to run on gaming GPUs.

It’s really not that big of a barrier. Yes, $100k in hardware is, but from a non-profit entity perspective that is peanuts.

Also adding a vision encoder for images to deep seek would not be theoretically that difficult for the same reason. In fact, I’m working on research right now that finds GPT4o and o1 have similar vision capabilities, implying it’s the same first layer vision encoder and then textual chain of thought tokens are read by subsequent layers. (This is a very recent insight as of last week by my team, so if anyone can disprove that, I would be very interested to know!)