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> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…
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Earlier quoted context omitted.
Correct. We need open weights, open code and open data. If nobody else can reproduce what someone did there will always be security questions. Even if we can reproduce it there could still be security concerns but it's more realistic to investigate yourself.
Exactly what are the possible 'security issues' of self hosting an open weights model?
Earlier quoted context omitted.
More like, is a professor who learned from books prohibited from writing his own books on the subject?
He is prohibited from regurgitating source material, of course! But if he generalized from the books he read and really learned the subject--and even made new connections between ideas--then he is free to write his own book.
The professor is free to lift all the facts and formula they want. They just need to rephrase explanations. Which is pretty much what an LLM is going to do.
According to openAI's own @deanwball: Even OpenAI isn't buying this distillation talk: https://xcancel.com/deanwball/status/2078133895766114412#m
Can you or someone please explain several of the claims made in this tweet? "I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks" what risks? I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means Open-weight models are inherently dec…
The Silicon Valley people like this openai guy, high on their own supply, are convinced they are building some machine god that will either bring about the end of the human race or utopia, they therefore cannot understand why the Chinese (or any other normal person on earth) are not afraid of chatbots and have other things on their minds.
The article makes a point about agent harnesses being sticky (the supposed moat). I have been building my own agent harness for a while, and I can tell with confidence that the harness almost does not matter, the entirety of the AI magic is the model itself. The harness can be almost barebones (like, for example, mini-swe-agent used for benchmarks), and yet the model still does the task just fine. So from my perspect…
He talks about this in another recent essay https://stratechery.com/2026/anthropics-safety-superpower/
> If you own the user touchpoint, then you have meaningful lock-in, and the best way to own the user touchpoint is to be the canvas for everything they need to do. This, by extension, means that the frontier labs are on a collision course with software companies: it’s software that owns the user touchpoint, and it’s in the frontier labs’ long-term interest to not simply be a commodity input into software but to simply replace software outright.
> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…
Don’t know much about how distillation works so please enlighten me here. > what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models If it’s as easy as that why do they choose to distill another model and not distill the knowledge on the open Internet from scratch?
> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…
Seems only fair that if LLMs can use copyrighted data for training then they should be able to use cannot-be-copyrighted output of other LLMs. But barring the terms of service from forbidding distillation seems like a tough sell. OpenAI shouldn't be allowed to decide what types of customers it wants and doesn't want?
> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…
Forbidding distillation is like forbidding using a compiler to make another(perhaps better, more efficient) compiler.
This from OpenAi's Head of Strategic Futures "Some observations on Kimi: It's a very good model! I don't think its performance can be explained away by distillation or anything like that"
https://x.com/deanwball/status/2078133895766114412
China's strategy of spending billions on training these models and open sourcing these models away is strategic - they want to kill the US LLM industry at any cost.
To win on the AI front by any means necessary.